PSC article analysis Q1

1. Hypothesis: PSC is associated with the gut microbiome changes.

Pre_LTx vs Post_LTx vs Healthy analysis on merged data

• Alpha diversity –> group effect, country effect, interaction effect

• Beta diversity – PERMANOVA, PCA -> group effect, country effect, interaction effect

• DAA ->

  • o Group effect – linDA + MaAsLin2 intersection

  • o Country effect – linDA + MaAsLin2 union

Pre_ltx vs Healthy – by this comparison, we will find the effect of disease, liver cirrhosis and overall bad clinical condition

Pre_ltx vs post_ltx – by this comparison, we will find the effect of transplantation + general improvement of the clinical state

Post_ltx vs Healthy – by this comparison, we will find if the transplantation leads to „healthy microbiome”

TO DO —- Hopefully, we will find that all of these groups are somehow significantly different.

Regarding Post_LTx vs Healthy analysis, we can assume, that the differentially abundant taxa between these two groups remain because of the persistent gut MB alteration due to the PSC disease. Also, transplantation itself could add to this difference, as it has definitely strong impact on microbial composition.

Regarding Pre_LTx vs Healthy analysis, we can assume, that the differentially abundant taxa between these two groups are caused by PSC disease and other factors, like liver cirrhosis and overall bad clinical condition. To get only PSC associated taxa, we can intersect differentially abundant taxa from these aforementioned analyses (see diagram below).

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TO DO: IMPORTOVAT TABULKU 1

source("custom_functions.R")

Data Import

Importing ASV, taxa and metadata tables for both Czech and Norway samples.

Czech

path = "../../data/analysis_ready_data/ikem/"
asv_tab_ikem <- as.data.frame(fread(file.path(path,"asv_table_ikem.csv"),
                                    check.names = FALSE))
taxa_tab_ikem <- as.data.frame(fread(file.path(path,"taxa_table_ikem.csv"),
                                     check.names = FALSE))
metadata_ikem <- as.data.frame(fread(file.path(path,"metadata_ikem.csv"),
                                     check.names = FALSE))

Norway

path = "../../data/analysis_ready_data/norway/"
asv_tab_norway <- as.data.frame(fread(file.path(path,"asv_table_norway.csv"),
                                    check.names = FALSE))
taxa_tab_norway <- as.data.frame(fread(file.path(path,"taxa_table_norway.csv"),
                                    check.names = FALSE))
metadata_norway <- as.data.frame(fread(file.path(path,"metadata_norway.csv"),
                                    check.names = FALSE))

Merging

TO DO: STATISTICS OF READS

Merging two countries based on the different matrices - Ileum, Colon.

Terminal ileum

ileum_data <- merging_data(asv_tab_1=asv_tab_ikem,
                           asv_tab_2=asv_tab_norway,
                           taxa_tab_1=taxa_tab_ikem,
                           taxa_tab_2=taxa_tab_norway,
                           metadata_1=metadata_ikem,
                           metadata_2=metadata_norway,
                           segment="TI",Q="Q1")

## Removing 1498 ASV(s)

## Removing 1834 ASV(s)

## Merging at ASV level

## Finding inconsistencies in taxonomy, trying to keep the ones that have better taxonomy assignment

ileum_asv_tab <- ileum_data[[1]]
ileum_taxa_tab <- ileum_data[[2]]
ileum_metadata <- ileum_data[[3]]

Colon

colon_data <- merging_data(asv_tab_1=asv_tab_ikem,
                           asv_tab_2=asv_tab_norway,
                           taxa_tab_1=taxa_tab_ikem,
                           taxa_tab_2=taxa_tab_norway,
                           metadata_1=metadata_ikem,
                           metadata_2=metadata_norway,
                           segment="colon",Q="Q1")

## Removing 739 ASV(s)

## Removing 266 ASV(s)

## Merging at ASV level

## Finding inconsistencies in taxonomy, trying to keep the ones that have better taxonomy assignment

colon_asv_tab <- colon_data[[1]]
colon_taxa_tab <- colon_data[[2]]
colon_metadata <- colon_data[[3]]

Data Analysis - Terminal ileum

segment="terminal_ileum"

Filtering

Rules: - prevalence > 5% (per group) - nearZeroVar with default settings - sequencing depth > 5000 - taxonomic assignment at least order

Rarefaction Curve

path="../intermediate_files/rarecurves"
seq_depth_threshold <- 10000

ps <- construct_phyloseq(ileum_asv_tab,ileum_taxa_tab,ileum_metadata)
rareres <- get_rarecurve(obj=ps, chunks=500)
save(rareres,file = file.path(path,"rarefaction_ileum.Rdata"))

load(file.path(path,"rarefaction_ileum.Rdata"))

prare <- ggrarecurve(obj=rareres,
                      factorNames="Country",
                      indexNames=c("Observe")) + 
  theme_bw() +
  theme(axis.text=element_text(size=8), panel.grid=element_blank(),
        strip.background = element_rect(colour=NA,fill="grey"),
        strip.text.x = element_text(face="bold")) + 
  geom_vline(xintercept = seq_depth_threshold, 
             linetype="dashed", 
             color = "red") + 
  xlim(0, 20000)

## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs produced

## The color has been set automatically, you can reset it manually by adding scale_color_manual(values=yourcolors)

prare

## Warning: Removed 10678 rows containing non-finite outside the scale range (`stat_smooth()`).

## Warning: The following aesthetics were dropped during statistical transformation: ymin and ymax.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical variable into a factor?

Library size

read_counts(ileum_asv_tab, line = c(5000,10000))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

Sequencing depth

data_filt <- seq_depth_filtering(ileum_asv_tab,
                                 ileum_taxa_tab,
                                 ileum_metadata,
                                 seq_depth_threshold = 10000)

## Removing 131 ASV(s)

filt_ileum_asv_tab <- data_filt[[1]]; alpha_ileum_asv_tab <- filt_ileum_asv_tab
filt_ileum_taxa_tab <- data_filt[[2]]; alpha_ileum_taxa_tab <- filt_ileum_taxa_tab
filt_ileum_metadata <- data_filt[[3]]; alpha_ileum_metadata <- filt_ileum_metadata

seq_step <- dim(filt_ileum_asv_tab)[1]

Library size

read_counts(filt_ileum_asv_tab,line = c(5000,10000))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

NearZeroVar

data_filt <- nearzerovar_filtering(filt_ileum_asv_tab, 
                                   filt_ileum_taxa_tab,
                                   filt_ileum_metadata)

filt_ileum_asv_tab <- data_filt[[1]]
filt_ileum_taxa_tab <- data_filt[[2]]
nearzero_step <- dim(filt_ileum_asv_tab)[1]

Library size

read_counts(filt_ileum_asv_tab,line = c(5000,10000))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

Final Counts

final_counts_filtering(ileum_asv_tab,
                       filt_ileum_asv_tab,
                       filt_ileum_metadata,
                       seq_step, 0, nearzero_step) %>% `colnames<-`("Count")

Alpha diversity

path = "../results/Q1/alpha_diversity"

Calculation

# Construct MPSE object
alpha_ileum_metadata$Sample <- alpha_ileum_metadata$SampleID
ileum_mpse <- as.MPSE(construct_phyloseq(alpha_ileum_asv_tab,
                                         alpha_ileum_taxa_tab,
                                         alpha_ileum_metadata))

ileum_mpse %<>% mp_rrarefy(raresize = 10000,seed = 123)

# Calculate alpha diversity - rarefied counts
ileum_mpse %<>% mp_cal_alpha(.abundance=RareAbundance, force=TRUE)

alpha_div_plots <- list()

# preparing data frame
alpha_data <- data.frame(SampleID=ileum_mpse$Sample.x,
                         Observe=ileum_mpse$Observe,
                         Shannon=ileum_mpse$Shannon,
                         Simpson=ileum_mpse$Simpson,
                         Pielou=ileum_mpse$Pielou,
                         Group=ileum_mpse$Group,
                         Country=ileum_mpse$Country)

write.csv(alpha_data,file.path(path,paste0("alpha_indices_",segment,".csv")),
          row.names = FALSE)

Plots

Country plot

labels = data.frame(country=rep(c("CZ","NO"),3),
                   x=c(0.8,1.19,1.8,2.19,2.8,3.19),
                   y=rep(0.2,6),
                   label=rep(c("CZ","NO"),3))

p_boxplot_alpha <- boxplot_subgroups(alpha_data,labels)

## Using SampleID, Group, Country as id variables

# save the results
alpha_div_plots[[paste(segment,"Country")]] <- p_boxplot_alpha

# see the results
p_boxplot_alpha

Custom plot

alpha_data <- alpha_data %>% 
  dplyr::select(-c("Simpson","Pielou")) %>%
  mutate(Richness=Observe)

p_A <- alpha_diversity_custom_2(alpha_data,
                                size = 1.5,
                                width = 0.3)

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

# save the results
alpha_div_plots[[paste(segment,"Custom")]] <- p_A

p_A

Linear Model

path = "../results/Q1/alpha_diversity"
alpha_data <- read.csv(file.path(path,paste0("alpha_indices_",segment,".csv")))

Richness

results_model <- pairwise.lm(formula = "Observe ~ Group * Country",
                             factors=alpha_data$Group,
                             data=alpha_data)

# check interaction
if (!is.data.frame(results_model)){
  results_model_observe <- results_model[[1]]
  results_model_observe_emeans <- results_model[[2]]
} else {
  results_model_observe <- results_model
  results_model_observe_emeans <- NA
}

# save the results
pc_observed <- list(); 
pc_observed[[segment]] <- results_model_observe

# see the results
knitr::kable(results_model_observe,digits = 3,
caption = "Raw results of linear model of richness estimation.")

Raw results of linear model of richness estimation.

	Estimate	Std..Error	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-53.642	11.742	-4.568	0.000	0.000	***
healthy , pre_ltx - CZ vs NO	11.219	10.488	1.070	0.287	0.361
healthy vs Grouppre_ltx:CountryNO	6.313	15.403	0.410	0.683	0.683
pre_ltx vs Grouppost_ltx	25.623	11.366	2.254	0.025	0.057
pre_ltx , post_ltx - CZ vs NO	17.533	12.615	1.390	0.166	0.249
pre_ltx vs Grouppost_ltx:CountryNO	-35.933	15.921	-2.257	0.025	0.057
healthy vs Grouppost_ltx	-28.019	9.237	-3.033	0.003	0.012	*
healthy , post_ltx - CZ vs NO	11.219	11.267	0.996	0.321	0.361
healthy vs Grouppost_ltx:CountryNO	-29.620	14.629	-2.025	0.044	0.080

knitr::kable(results_model_observe_emeans,digits = 3,
caption = "Raw results of independent country analysis")

Raw results of independent country analysis

contrast	Country	estimate	SE	df	t.ratio	p.value
pre_ltx - post_ltx	CZ	-25.623	11.366	204	-2.254	0.025
pre_ltx - post_ltx	NO	10.310	11.148	204	0.925	0.356
healthy - post_ltx	CZ	28.019	9.237	207	3.033	0.003
healthy - post_ltx	NO	57.639	11.344	207	5.081	0.000

Shannon

results_model <- pairwise.lm(formula = "Shannon ~ Group * Country",
                             factors=alpha_data$Group,
                             data=alpha_data)

# check interaction
if (!is.data.frame(results_model)){
  results_model_shannon <- results_model[[1]]
  results_model_shannon_emeans <- results_model[[2]]
} else {
  results_model_shannon <- results_model
  results_model_shannon_emeans <- NA
}

# save the results
pc_shannon <- list(); 
pc_shannon[[segment]] <- as.data.frame(results_model_shannon)

# see the results
knitr::kable(results_model_shannon,digits = 3,
caption = "Raw results of linear model of Shannon estimation.")

Raw results of linear model of Shannon estimation.

	Estimate	Std..Error	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-0.505	0.159	-3.171	0.002	0.017	*
healthy , pre_ltx - CZ vs NO	0.007	0.142	0.046	0.963	0.963
healthy vs Grouppre_ltx:CountryNO	0.144	0.209	0.687	0.493	0.634
pre_ltx vs Grouppost_ltx	0.372	0.148	2.514	0.013	0.049	*
pre_ltx , post_ltx - CZ vs NO	0.150	0.164	0.915	0.361	0.542
pre_ltx vs Grouppost_ltx:CountryNO	-0.502	0.207	-2.423	0.016	0.049	*
healthy vs Grouppost_ltx	-0.134	0.111	-1.200	0.231	0.416
healthy , post_ltx - CZ vs NO	0.007	0.136	0.048	0.962	0.963
healthy vs Grouppost_ltx:CountryNO	-0.358	0.176	-2.031	0.044	0.098

knitr::kable(results_model_shannon_emeans,digits = 3,
caption = "Raw results of independent country analysis")

Raw results of independent country analysis

contrast	Country	estimate	SE	df	t.ratio	p.value
pre_ltx - post_ltx	CZ	-0.372	0.148	204	-2.514	0.013
pre_ltx - post_ltx	NO	0.130	0.145	204	0.898	0.370
healthy - post_ltx	CZ	0.134	0.111	207	1.200	0.231
healthy - post_ltx	NO	0.492	0.137	207	3.597	0.000

Simpson

results_model <- pairwise.lm(formula = "Simpson ~ Group * Country",
                                     factors=alpha_data$Group,
                                     data=alpha_data)

# check interaction
if (!is.data.frame(results_model)){
  results_model_simpson <- results_model[[1]]
  results_model_simpson_emeans <- results_model[[2]]
} else {
  results_model_simpson <- results_model
  results_model_simpson_emeans <- NA
}


# save the results
pc_simpson <- list(); 
pc_simpson[[segment]] <- as.data.frame(results_model_simpson)

# see the results
knitr::kable(results_model_simpson,digits = 3,
caption = "Raw results of linear model of Simpson estimation.")

Raw results of linear model of Simpson estimation.

	Estimate	Std..Error	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-0.046	0.019	-2.481	0.014	0.129
healthy , pre_ltx - CZ vs NO	-0.008	0.017	-0.466	0.642	0.692
healthy vs Grouppre_ltx:CountryNO	0.027	0.024	1.092	0.277	0.498
pre_ltx vs Grouppost_ltx	0.037	0.021	1.763	0.079	0.238
pre_ltx , post_ltx - CZ vs NO	0.019	0.023	0.811	0.418	0.627
pre_ltx vs Grouppost_ltx:CountryNO	-0.060	0.029	-2.033	0.043	0.195
healthy vs Grouppost_ltx	-0.009	0.016	-0.572	0.568	0.692
healthy , post_ltx - CZ vs NO	-0.008	0.020	-0.396	0.692	0.692
healthy vs Grouppost_ltx:CountryNO	-0.033	0.025	-1.303	0.194	0.436

knitr::kable(results_model_simpson_emeans,digits = 3,
caption = "Raw results of independent country analysis")

Raw results of independent country analysis

contrast	Country	estimate	SE	df	t.ratio	p.value
pre_ltx - post_ltx	CZ	-0.037	0.021	204	-1.763	0.079
pre_ltx - post_ltx	NO	0.023	0.021	204	1.106	0.270

Pielou

results_model <- pairwise.lm(formula = "Pielou ~ Group * Country",
                                     factors=alpha_data$Group,
                                     data=alpha_data)

# check interaction

if (!is.data.frame(results_model)){
  results_model_pielou <- results_model[[1]]
  results_model_pielou_emeans <- results_model[[2]]
} else {
  results_model_pielou <- results_model
  results_model_pielou_emeans <- NA
}

# save the results
pc_pielou <- list(); 
pc_pielou[[segment]] <- as.data.frame(results_model_pielou)

# see the results
knitr::kable(results_model_pielou,digits = 3,
caption = "Raw results of linear model of Pielou estimation.")

Raw results of linear model of Pielou estimation.

	Estimate	Std..Error	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-0.040	0.024	-1.708	0.090	0.285
healthy , pre_ltx - CZ vs NO	-0.008	0.021	-0.394	0.694	0.943
healthy vs Grouppre_ltx:CountryNO	0.010	0.031	0.332	0.740	0.943
pre_ltx vs Grouppost_ltx	0.042	0.022	1.922	0.056	0.285
pre_ltx , post_ltx - CZ vs NO	0.002	0.024	0.082	0.935	0.943
pre_ltx vs Grouppost_ltx:CountryNO	-0.051	0.030	-1.677	0.095	0.285
healthy vs Grouppost_ltx	0.001	0.017	0.072	0.943	0.943
healthy , post_ltx - CZ vs NO	-0.008	0.020	-0.407	0.684	0.943
healthy vs Grouppost_ltx:CountryNO	-0.041	0.027	-1.525	0.129	0.290

knitr::kable(results_model_pielou_emeans,digits = 3,
caption = "Raw results of independent country analysis")

Table: Raw results of independent country analysis ### Saving results

alpha_list <- list(
  Richness=pc_observed[[segment]] %>% rownames_to_column("Comparison"),
  Shannon=pc_shannon[[segment]] %>% rownames_to_column("Comparison"),
  Simpson=pc_simpson[[segment]] %>% rownames_to_column("Comparison"),
  Pielou=pc_pielou[[segment]] %>% rownames_to_column("Comparison"))
                   
write.xlsx(alpha_list, 
           file = file.path(path,paste0("alpha_diversity_results_",segment,".xlsx")))

Beta diversity

Calculating Aitchison distance (euclidean distance on clr-transformed data), both at ASV and genus level.

Main analysis - Genus, Aitchison

Genus level, Aitchison distance

level="genus"

path = "../results/Q1/beta_diversity"

pairwise_aitchison_raw <- list()
pca_plots_list <- list()

Aggregation, filtering

# Aggregation
genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level=level,
                             names=TRUE)
# Filtration
filt_data <- filtering_steps(genus_data[[1]],
                             genus_data[[2]],
                             ileum_metadata,
                             seq_depth_threshold=10000)

## Removing 5 ASV(s)

filt_ileum_genus_tab <- filt_data[[1]]
filt_ileum_genus_taxa <- filt_data[[2]]
filt_ileum_metadata <- filt_data[[3]]

PERMANOVA

pairwise_df <- filt_ileum_genus_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,
                           filt_ileum_metadata$Group,
                           covariate = filt_ileum_metadata$Country, sim.method = "robust.aitchison", p.adjust.m="BH")

# interaction
pp_int <- pairwise.adonis(pairwise_df,filt_ileum_metadata$Group,covariate = filt_ileum_metadata$Country, interaction = TRUE, sim.method = "robust.aitchison", p.adjust.m="BH")

# tidy the results
pp_factor <- pp_main[[1]]
pp_cov <- pp_main[[2]]
pp_fac.cov <- pp_int[[3]]

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols; colnames(pp_cov) <- cols; colnames(pp_fac.cov) <- cols; 

# save raw results
pairwise_aitchison_raw[[paste(level, segment)]] <- rbind(pp_factor,pp_cov,pp_fac.cov)

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	695.306	4.259	0.029	0.001	0.002	**
pre_ltx vs post_ltx	1	309.768	1.811	0.009	0.003	0.003	**
post_ltx vs healthy	1	914.409	5.454	0.025	0.001	0.002	**

knitr::kable(pp_cov,digits = 3,caption = "PERMANOVA, COUNTRY separation")

PERMANOVA, COUNTRY separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy , Country	1	482.074	2.953	0.020	0.001	0.001	***
pre_ltx vs post_ltx , Country	1	699.075	4.087	0.019	0.001	0.001	***
post_ltx vs healthy , Country	1	728.020	4.342	0.020	0.001	0.001	***

knitr::kable(pp_fac.cov,digits = 3,caption = "PERMANOVA, INTERACTION GROUP:Country")

PERMANOVA, INTERACTION GROUP:Country

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy : Country	1	237.598	1.460	0.010	0.010	0.030	*
pre_ltx vs post_ltx : Country	1	237.265	1.390	0.007	0.021	0.032	*
post_ltx vs healthy : Country	1	202.888	1.211	0.006	0.106	0.106

Interaction check

interaction_sig <- pp_fac.cov$pairs[pp_fac.cov$p.adjusted < 0.05]

for (i in 1:length(interaction_sig)){
  group1 <- unlist(strsplit(interaction_sig[i],split = " vs "))[1]
  group2 <- unlist(strsplit(interaction_sig[i],split = " vs "))[2]
  group2 <- unlist(strsplit(group2,split = " : "))[1]
  
  result_list <- adonis_postanalysis(x=pairwise_df,
                      factors = filt_ileum_metadata$Group,
                      covariate = filt_ileum_metadata$Country, 
                      group1 = group1,
                      group2 = group2)
  print(result_list)
}

## $pre_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1    275.1 0.02844 1.7272  0.003      0.003
## Residual 59   9396.0 0.97156                         
## Total    60   9671.1 1.00000                         
## 
## $pre_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs     R2      F Pr(>F) p.adjusted
## Fac       1    657.8 0.0474 3.9806  0.001  0.0013333
## Residual 80  13220.8 0.9526                         
## Total    81  13878.7 1.0000                         
## 
## $pre_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2     F Pr(>F) p.adjusted
## Cov       1    362.6 0.03086 2.165  0.001  0.0013333
## Residual 68  11387.2 0.96914                        
## Total    69  11749.8 1.00000                        
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1    357.1 0.03082 2.2579  0.001  0.0013333
## Residual 71  11229.6 0.96918                         
## Total    72  11586.7 1.00000                         
## 
## $pre_ltx_post_ltx_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs     R2      F Pr(>F) p.adjusted
## Fac        1    184.3 0.0085 1.0629  0.314      0.314
## Residual 124  21497.3 0.9915                         
## Total    125  21681.6 1.0000                         
## 
## $pre_ltx_post_ltx_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1    362.8 0.02649 2.1767  0.001  0.0013333
## Residual 80  13332.7 0.97351                         
## Total    81  13695.4 1.00000                         
## 
## $pre_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2     F Pr(>F) p.adjusted
## Cov       1    362.6 0.03086 2.165  0.001  0.0013333
## Residual 68  11387.2 0.96914                        
## Total    69  11749.8 1.00000                        
## 
## $post_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1    573.8 0.02389 3.3288  0.001  0.0013333
## Residual 136  23442.8 0.97611                         
## Total    137  24016.6 1.00000

Plots

Custom

p <- pca_plot_custom(filt_ileum_genus_tab,
                                 filt_ileum_genus_taxa,
                                 filt_ileum_metadata,
                                 show_boxplots = TRUE,
                                 variable = "Group", size=3, show_legend=TRUE)

# save the results
pca_plots_list[[paste(segment,level,"custom")]] <- p

# see the results
p

Saving results

write.xlsx(pairwise_aitchison_raw[[paste(level, segment)]], 
           file = file.path(path,
           paste0("beta_diversity_results_", segment,".xlsx")))

Supplementary analysis

supplements_beta <- list()

Genus level

level="genus"

Bray-Curtis

PERMANOVA

pairwise_df <- filt_ileum_genus_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_ileum_metadata$Group,covariate = filt_ileum_metadata$Country, sim.method = "bray", p.adjust.m="BH")

# interaction
pp_int <- pairwise.adonis(pairwise_df,filt_ileum_metadata$Group,covariate = filt_ileum_metadata$Country, interaction = TRUE, sim.method = "bray", p.adjust.m="BH")

# tidy the results
pp_factor <- pp_main[[1]]
pp_cov <- pp_main[[2]]
pp_fac.cov <- pp_int[[3]]

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols; colnames(pp_cov) <- cols; colnames(pp_fac.cov) <- cols; 

# save raw results
supplements_beta[[paste("bray",level,segment)]] <- rbind(pp_factor,pp_cov,pp_fac.cov)

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	0.963	5.058	0.033	0.001	0.002	**
pre_ltx vs post_ltx	1	0.342	1.581	0.007	0.070	0.070
post_ltx vs healthy	1	1.551	7.718	0.034	0.001	0.002	**

knitr::kable(pp_cov,digits = 3,caption = "PERMANOVA, COUNTRY separation")

PERMANOVA, COUNTRY separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy , Country	1	1.132	5.948	0.039	0.001	0.001	***
pre_ltx vs post_ltx , Country	1	1.991	9.200	0.042	0.001	0.001	***
post_ltx vs healthy , Country	1	1.656	8.242	0.037	0.001	0.001	***

knitr::kable(pp_fac.cov,digits = 3,caption = "PERMANOVA, INTERACTION GROUP:Country")

PERMANOVA, INTERACTION GROUP:Country

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy : Country	1	0.449	2.380	0.016	0.002	0.006	**
pre_ltx vs post_ltx : Country	1	0.330	1.527	0.007	0.075	0.075
post_ltx vs healthy : Country	1	0.376	1.878	0.008	0.013	0.020	*

Interaction check

interaction_sig <- pp_fac.cov$pairs[pp_fac.cov$p.adjusted < 0.05]

for (i in 1:length(interaction_sig)){
  group1 <- unlist(strsplit(interaction_sig[i],split = " vs "))[1]
  group2 <- unlist(strsplit(interaction_sig[i],split = " vs "))[2]
  group2 <- unlist(strsplit(group2,split = " : "))[1]
  
  result_list <- adonis_postanalysis(x=pairwise_df,
                      factors = filt_ileum_metadata$Group,
                      covariate = filt_ileum_metadata$Country, 
                      group1 = group1,
                      group2 = group2,
                      sim.method = 'bray')
  print(result_list)
}

## $pre_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs     R2      F Pr(>F) p.adjusted
## Fac       1   0.4144 0.0363 2.2222  0.006      0.006
## Residual 59  11.0035 0.9637                         
## Total    60  11.4180 1.0000                         
## 
## $pre_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1   0.9972 0.06156 5.2483  0.001  0.0013333
## Residual 80  15.2005 0.93844                         
## Total    81  16.1977 1.00000                         
## 
## $pre_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1   0.9344 0.06093 4.4123  0.001  0.0013333
## Residual 68  14.4010 0.93907                         
## Total    69  15.3354 1.00000                         
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1   0.6466 0.05193 3.8893  0.001  0.0013333
## Residual 71  11.8030 0.94807                         
## Total    72  12.4496 1.00000                         
## 
## $post_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1   0.7698 0.02676 3.7676  0.001      0.001
## Residual 137  27.9925 0.97324                         
## Total    138  28.7623 1.00000                         
## 
## $post_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs     R2      F Pr(>F) p.adjusted
## Fac       1   1.1573 0.0793 6.0291  0.001      0.001
## Residual 70  13.4368 0.9207                         
## Total    71  14.5941 1.0000                         
## 
## $post_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1   1.3859 0.04469 6.3619  0.001      0.001
## Residual 136  29.6263 0.95531                         
## Total    137  31.0121 1.00000                         
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1   0.6466 0.05193 3.8893  0.001      0.001
## Residual 71  11.8030 0.94807                         
## Total    72  12.4496 1.00000

Plots

p <- pca_plot_custom(filt_ileum_genus_tab,
                                 filt_ileum_genus_taxa,
                                 filt_ileum_metadata,
                                 measure = "bray",
                                 show_boxplots = TRUE,
                                 variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA bray",level,segment)]] <- p

# see the results
p

Jaccard

PERMANOVA

pairwise_df <- filt_ileum_genus_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_ileum_metadata$Group,covariate = filt_ileum_metadata$Country, sim.method = "jaccard", p.adjust.m="BH")

# interaction
pp_int <- pairwise.adonis(pairwise_df,filt_ileum_metadata$Group,covariate = filt_ileum_metadata$Country, interaction = TRUE, sim.method = "jaccard", p.adjust.m="BH")

# tidy the results
pp_factor <- pp_main[[1]]
pp_cov <- pp_main[[2]]
pp_fac.cov <- pp_int[[3]]

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols; colnames(pp_cov) <- cols; colnames(pp_fac.cov) <- cols; 

# save raw results
supplements_beta[[paste("jaccard",level,segment)]] <- rbind(pp_factor, 
                                                            pp_cov, 
                                                            pp_fac.cov)

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	1.087	3.929	0.026	0.001	0.002	**
pre_ltx vs post_ltx	1	0.439	1.457	0.007	0.045	0.045	*
post_ltx vs healthy	1	1.633	5.675	0.026	0.001	0.002	**

knitr::kable(pp_cov,digits = 3,caption = "PERMANOVA, COUNTRY separation")

PERMANOVA, COUNTRY separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy , Country	1	1.232	4.452	0.030	0.001	0.001	***
pre_ltx vs post_ltx , Country	1	1.965	6.513	0.030	0.001	0.001	***
post_ltx vs healthy , Country	1	1.751	6.085	0.028	0.001	0.001	***

knitr::kable(pp_fac.cov,digits = 3,caption = "PERMANOVA, INTERACTION GROUP:Country")

PERMANOVA, INTERACTION GROUP:Country

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy : Country	1	0.525	1.912	0.013	0.002	0.006	**
pre_ltx vs post_ltx : Country	1	0.405	1.346	0.006	0.072	0.072
post_ltx vs healthy : Country	1	0.450	1.569	0.007	0.017	0.026	*

Interaction check

interaction_sig <- pp_fac.cov$pairs[pp_fac.cov$p.adjusted < 0.05]

for (i in 1:length(interaction_sig)){
  group1 <- unlist(strsplit(interaction_sig[i],split = " vs "))[1]
  group2 <- unlist(strsplit(interaction_sig[i],split = " vs "))[2]
  group2 <- unlist(strsplit(group2,split = " : "))[1]
  
  result_list <- adonis_postanalysis(x=pairwise_df,
                      factors = filt_ileum_metadata$Group,
                      covariate = filt_ileum_metadata$Country, 
                      group1 = group1,
                      group2 = group2,
                      sim.method = 'jaccard')
  print(result_list)
}

## $pre_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1   0.5009 0.03035 1.8467  0.007      0.007
## Residual 59  16.0018 0.96965                         
## Total    60  16.5027 1.00000                         
## 
## $pre_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1   1.1117 0.04768 4.0054  0.001  0.0013333
## Residual 80  22.2037 0.95232                         
## Total    81  23.3154 1.00000                         
## 
## $pre_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1   0.9632 0.04568 3.2547  0.001  0.0013333
## Residual 68  20.1235 0.95432                         
## Total    69  21.0867 1.00000                         
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1    0.794 0.04206 3.1176  0.001  0.0013333
## Residual 71   18.082 0.95794                         
## Total    72   18.876 1.00000                         
## 
## $post_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    0.865 0.02135 2.9894  0.001      0.001
## Residual 137   39.627 0.97865                         
## Total    138   40.491 1.00000                         
## 
## $post_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1   1.2181 0.05806 4.3147  0.001      0.001
## Residual 70  19.7616 0.94194                         
## Total    71  20.9797 1.00000                         
## 
## $post_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1    1.407 0.03294 4.6318  0.001      0.001
## Residual 136   41.306 0.96706                         
## Total    137   42.713 1.00000                         
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1    0.794 0.04206 3.1176  0.001      0.001
## Residual 71   18.082 0.95794                         
## Total    72   18.876 1.00000

Plots

Custom

p <- pca_plot_custom(filt_ileum_genus_tab,
                                 filt_ileum_genus_taxa,
                                 filt_ileum_metadata,
                                 measure = "jaccard",
                                 show_boxplots = TRUE,
                                 variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA jaccard",level,segment)]] <- p

# see the results
p

ASV level

level="ASV"

Aitchison

PERMANOVA

# preparing data frame
pairwise_df <- filt_ileum_asv_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_ileum_metadata$Group,covariate = filt_ileum_metadata$Country, sim.method = "robust.aitchison", p.adjust.m="BH")

# interaction
pp_int <- pairwise.adonis(pairwise_df,filt_ileum_metadata$Group,covariate = filt_ileum_metadata$Country, interaction = TRUE, sim.method = "robust.aitchison", p.adjust.m="BH")

pp_factor <- pp_main[[1]]
pp_cov <- pp_main[[2]]
pp_fac.cov <- pp_int[[3]]

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols; colnames(pp_cov) <- cols; colnames(pp_fac.cov) <- cols; 

# save raw results
supplements_beta[[paste("aitchison",level,segment)]] <- rbind(pp_factor, 
                                                            pp_cov, 
                                                            pp_fac.cov)

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	1010.000	3.749	0.026	0.001	0.002	**
pre_ltx vs post_ltx	1	364.559	1.485	0.007	0.003	0.003	**
post_ltx vs healthy	1	1389.311	5.466	0.025	0.001	0.002	**

knitr::kable(pp_cov,digits = 3,caption = "PERMANOVA, COUNTRY separation")

PERMANOVA, COUNTRY separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy , Country	1	603.397	2.239	0.015	0.001	0.001	***
pre_ltx vs post_ltx , Country	1	671.339	2.734	0.013	0.001	0.001	***
post_ltx vs healthy , Country	1	789.105	3.105	0.014	0.001	0.001	***

knitr::kable(pp_fac.cov,digits = 3,caption = "PERMANOVA, INTERACTION GROUP:Country")

PERMANOVA, INTERACTION GROUP:Country

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy : Country	1	364.013	1.354	0.009	0.014	0.021	*
pre_ltx vs post_ltx : Country	1	352.491	1.439	0.007	0.007	0.021	*
post_ltx vs healthy : Country	1	281.750	1.109	0.005	0.170	0.170

Interaction check

interaction_sig <- pp_fac.cov$pairs[pp_fac.cov$p.adjusted < 0.05]

for (i in 1:length(interaction_sig)){
  group1 <- unlist(strsplit(interaction_sig[i],split = " vs "))[1]
  group2 <- unlist(strsplit(interaction_sig[i],split = " vs "))[2]
  group2 <- unlist(strsplit(group2,split = " : "))[1]
  
  result_list <- adonis_postanalysis(x=pairwise_df,
                      factors = filt_ileum_metadata$Group,
                      covariate = filt_ileum_metadata$Country, 
                      group1 = group1,
                      group2 = group2)
  print(result_list)
}

## $pre_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1      420 0.02629 1.5929  0.004      0.004
## Residual 59    15557 0.97371                         
## Total    60    15977 1.00000                         
## 
## $pre_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1      954 0.04193 3.5009  0.001      0.002
## Residual 80    21800 0.95807                         
## Total    81    22754 1.00000                         
## 
## $pre_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1    460.2 0.02579 1.8004  0.002  0.0026667
## Residual 68  17380.9 0.97421                         
## Total    69  17841.1 1.00000                         
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1    507.2 0.02476 1.8028  0.001      0.002
## Residual 71  19976.2 0.97524                         
## Total    72  20483.4 1.00000                         
## 
## $pre_ltx_post_ltx_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    266.4 0.00872 1.0904  0.221      0.221
## Residual 124  30296.1 0.99128                         
## Total    125  30562.5 1.00000                         
## 
## $pre_ltx_post_ltx_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1    450.6 0.02237 1.8308  0.001      0.002
## Residual 80  19691.5 0.97763                         
## Total    81  20142.1 1.00000                         
## 
## $pre_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1    460.2 0.02579 1.8004  0.002  0.0026667
## Residual 68  17380.9 0.97421                         
## Total    69  17841.1 1.00000                         
## 
## $post_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1      564 0.01699 2.3509  0.001      0.002
## Residual 136    32607 0.98301                         
## Total    137    33170 1.00000

PCoA

p <- pca_plot_custom(filt_ileum_asv_tab,
                           filt_ileum_taxa_tab,
                           filt_ileum_metadata,
                           show_boxplots = TRUE,
                           variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA aitchison",level,segment)]] <- p

# see the results
p

Bray-Curtis

PERMANOVA

# preparing data frame
pairwise_df <- filt_ileum_asv_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_ileum_metadata$Group,covariate = filt_ileum_metadata$Country, sim.method = "bray", p.adjust.m="BH")

# interaction
pp_int <- pairwise.adonis(pairwise_df,filt_ileum_metadata$Group,covariate = filt_ileum_metadata$Country, interaction = TRUE, sim.method = "bray", p.adjust.m="BH")

pp_factor <- pp_main[[1]]
pp_cov <- pp_main[[2]]
pp_fac.cov <- pp_int[[3]]

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols; colnames(pp_cov) <- cols; colnames(pp_fac.cov) <- cols; 

# save raw results
supplements_beta[[paste("bray",level,segment)]] <- rbind(pp_factor, 
                                                            pp_cov, 
                                                            pp_fac.cov)

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	1.628	5.225	0.035	0.001	0.002	**
pre_ltx vs post_ltx	1	0.519	1.563	0.007	0.024	0.024	*
post_ltx vs healthy	1	2.428	7.667	0.035	0.001	0.002	**

knitr::kable(pp_cov,digits = 3,caption = "PERMANOVA, COUNTRY separation")

PERMANOVA, COUNTRY separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy , Country	1	1.136	3.644	0.024	0.001	0.001	***
pre_ltx vs post_ltx , Country	1	1.676	5.049	0.024	0.001	0.001	***
post_ltx vs healthy , Country	1	1.667	5.264	0.024	0.001	0.001	***

knitr::kable(pp_fac.cov,digits = 3,caption = "PERMANOVA, INTERACTION GROUP:Country")

PERMANOVA, INTERACTION GROUP:Country

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy : Country	1	0.469	1.511	0.010	0.027	0.040	*
pre_ltx vs post_ltx : Country	1	0.562	1.698	0.008	0.010	0.030	*
post_ltx vs healthy : Country	1	0.432	1.367	0.006	0.064	0.064

Interaction check

interaction_sig <- pp_fac.cov$pairs[pp_fac.cov$p.adjusted < 0.05]

for (i in 1:length(interaction_sig)){
  group1 <- unlist(strsplit(interaction_sig[i],split = " vs "))[1]
  group2 <- unlist(strsplit(interaction_sig[i],split = " vs "))[2]
  group2 <- unlist(strsplit(group2,split = " : "))[1]
  
  result_list <- adonis_postanalysis(x=pairwise_df,
                      factors = filt_ileum_metadata$Group,
                      covariate = filt_ileum_metadata$Country, 
                      group1 = group1,
                      group2 = group2,
                      sim.method = 'bray')
  print(result_list)
}

## $pre_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1   0.5756 0.02993 1.8204  0.005      0.005
## Residual 59  18.6557 0.97007                         
## Total    60  19.2313 1.00000                         
## 
## $pre_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2     F Pr(>F) p.adjusted
## Fac       1   1.5215 0.05848 4.969  0.001  0.0013333
## Residual 80  24.4953 0.94152                        
## Total    81  26.0168 1.00000                        
## 
## $pre_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1   0.8717 0.03713 2.6224  0.001  0.0013333
## Residual 68  22.6028 0.96287                         
## Total    69  23.4744 1.00000                         
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1    0.733 0.03444 2.5326  0.001  0.0013333
## Residual 71   20.548 0.96556                         
## Total    72   21.281 1.00000                         
## 
## $pre_ltx_post_ltx_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    0.430 0.01027 1.2865  0.114      0.114
## Residual 124   41.443 0.98973                         
## Total    125   41.873 1.00000                         
## 
## $pre_ltx_post_ltx_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1   0.6509 0.02437 1.9985  0.004  0.0053333
## Residual 80  26.0569 0.97563                         
## Total    81  26.7079 1.00000                         
## 
## $pre_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1   0.8717 0.03713 2.6224  0.001      0.002
## Residual 68  22.6028 0.96287                         
## Total    69  23.4744 1.00000                         
## 
## $post_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1    1.366 0.02953 4.1389  0.001      0.002
## Residual 136   44.897 0.97047                         
## Total    137   46.263 1.00000

PCoA

p <- pca_plot_custom(filt_ileum_asv_tab,
                     filt_ileum_taxa_tab,
                     filt_ileum_metadata,
                     measure = "bray",
                     show_boxplots = TRUE,
                     variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA bray",level,segment)]] <- p

# see the results
p

Jaccard

PERMANOVA

# preparing data frame
pairwise_df <- filt_ileum_asv_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_ileum_metadata$Group,covariate = filt_ileum_metadata$Country, sim.method = "jaccard", p.adjust.m="BH")

# interaction
pp_int <- pairwise.adonis(pairwise_df,filt_ileum_metadata$Group,covariate = filt_ileum_metadata$Country, interaction = TRUE, sim.method = "jaccard", p.adjust.m="BH")

pp_factor <- pp_main[[1]]
pp_cov <- pp_main[[2]]
pp_fac.cov <- pp_int[[3]]

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols; colnames(pp_cov) <- cols; colnames(pp_fac.cov) <- cols; 

# save raw results
supplements_beta[[paste("jaccard",level,segment)]] <- rbind(pp_factor, 
                                                            pp_cov, 
                                                            pp_fac.cov)

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	1.354	3.531	0.024	0.001	0.002	**
pre_ltx vs post_ltx	1	0.528	1.327	0.006	0.024	0.024	*
post_ltx vs healthy	1	1.921	4.964	0.023	0.001	0.002	**

knitr::kable(pp_cov,digits = 3,caption = "PERMANOVA, COUNTRY separation")

PERMANOVA, COUNTRY separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy , Country	1	0.994	2.593	0.018	0.001	0.001	***
pre_ltx vs post_ltx , Country	1	1.384	3.480	0.017	0.001	0.001	***
post_ltx vs healthy , Country	1	1.396	3.607	0.017	0.001	0.001	***

knitr::kable(pp_fac.cov,digits = 3,caption = "PERMANOVA, INTERACTION GROUP:Country")

PERMANOVA, INTERACTION GROUP:Country

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy : Country	1	0.514	1.344	0.009	0.020	0.030	*
pre_ltx vs post_ltx : Country	1	0.571	1.439	0.007	0.010	0.030	*
post_ltx vs healthy : Country	1	0.488	1.262	0.006	0.041	0.041	*

Interaction check

interaction_sig <- pp_fac.cov$pairs[pp_fac.cov$p.adjusted < 0.05]

for (i in 1:length(interaction_sig)){
  group1 <- unlist(strsplit(interaction_sig[i],split = " vs "))[1]
  group2 <- unlist(strsplit(interaction_sig[i],split = " vs "))[2]
  group2 <- unlist(strsplit(group2,split = " : "))[1]
  
  result_list <- adonis_postanalysis(x=pairwise_df,
                      factors = filt_ileum_metadata$Group,
                      covariate = filt_ileum_metadata$Country, 
                      group1 = group1,
                      group2 = group2,
                      sim.method = 'jaccard')
  print(result_list)
}

## $pre_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1   0.5659 0.02422 1.4643  0.006      0.006
## Residual 59  22.8020 0.97578                         
## Total    60  23.3680 1.00000                         
## 
## $pre_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1    1.302 0.04112 3.4309  0.001  0.0013333
## Residual 80   30.352 0.95888                         
## Total    81   31.654 1.00000                         
## 
## $pre_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2     F Pr(>F) p.adjusted
## Cov       1   0.7898 0.02836 1.985  0.001  0.0013333
## Residual 68  27.0550 0.97164                        
## Total    69  27.8447 1.00000                        
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1   0.7183 0.02679 1.9541  0.001  0.0013333
## Residual 71  26.0995 0.97321                         
## Total    72  26.8178 1.00000                         
## 
## $pre_ltx_post_ltx_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    0.468 0.00938 1.1736  0.115      0.115
## Residual 124   49.489 0.99062                         
## Total    125   49.957 1.00000                         
## 
## $pre_ltx_post_ltx_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1     0.63 0.01964 1.6025  0.003      0.004
## Residual 80    31.47 0.98036                         
## Total    81    32.10 1.00000                         
## 
## $pre_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2     F Pr(>F) p.adjusted
## Cov       1   0.7898 0.02836 1.985  0.001      0.002
## Residual 68  27.0550 0.97164                        
## Total    69  27.8447 1.00000                        
## 
## $post_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1    1.165 0.02116 2.9397  0.001      0.002
## Residual 136   53.904 0.97884                         
## Total    137   55.069 1.00000                         
## 
## $post_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    1.058 0.01938 2.7075  0.001      0.001
## Residual 137   53.521 0.98062                         
## Total    138   54.578 1.00000                         
## 
## $post_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac       1   1.3507 0.04853 3.5701  0.001      0.001
## Residual 70  26.4826 0.95147                         
## Total    71  27.8333 1.00000                         
## 
## $post_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1    1.165 0.02116 2.9397  0.001      0.001
## Residual 136   53.904 0.97884                         
## Total    137   55.069 1.00000                         
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##          Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov       1   0.7183 0.02679 1.9541  0.001      0.001
## Residual 71  26.0995 0.97321                         
## Total    72  26.8178 1.00000

PCoA

p <- pca_plot_custom(filt_ileum_asv_tab,
                     filt_ileum_taxa_tab,
                     filt_ileum_metadata,
                     measure = "jaccard",
                     show_boxplots = TRUE,
                     variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA jaccard",level,segment)]] <- p

# see the results
p

Saving results

write.xlsx(supplements_beta[!grepl("PCoA",names(supplements_beta))],
           file = file.path(path,
           paste0("supplements_beta_diversity_", segment,".xlsx")))

Univariate Analysis

Main - Genus level

level="genus"

# needed paths
path = "../results/Q1/univariate_analysis"
path_maaslin=file.path("../intermediate_files/maaslin/Q1",level)

# variables
raw_linda_results_genus <- list();
raw_linda_results_genus[[segment]] <- list()
linda_results_genus <- list(); 
linda_results_genus[[segment]] <- list()

# country and interaction problems
list_country_union <- list()
list_intersections <- list()
list_venns <- list()
uni_statistics <- list()

# workbook for final df
wb <- createWorkbook()

# PSC effect
psc_effect <- list()

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

ileum_genus_asv_taxa_tab <- create_asv_taxa_table(ileum_genus_tab,
                                                  ileum_genus_taxa_tab)

pre_ltx vs healthy

group <- c("healthy","pre_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_genus_tab,
                            ileum_genus_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 84 ASV(s)

## Removing 10 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group * Country')

## 0  features are filtered!
## The filtered data has  143  samples and  184  features will be tested!
## Imputation approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results_genus[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results_genus[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle("Country effect") 

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_ileum_uni_taxa) +
            ggtitle("Interaction")

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano2 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa,variable="Country") + 
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)

## Warning: Removed 29 rows containing missing values or values outside the scale range (`geom_text_repel()`).

## Warning: Removed 14 rows containing missing values or values outside the scale range (`geom_text_repel()`).

volcano

## Warning: ggrepel: 18 unlabeled data points (too many overlaps). Consider increasing max.overlaps

## Warning: ggrepel: 8 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_genus, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_ileum_uni_data,
                                          filt_ileum_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_genus[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "pre_ltx"
## [1] "healthy"
## [1] "pre_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- comparison_name
addWorksheet(wb, sheetName = new_name)
writeData(wb, sheet = new_name, uni_df, rowNames=FALSE)

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_genus_tab,
                            ileum_genus_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 46 ASV(s)

## Removing 6 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group * Country')

## 0  features are filtered!
## The filtered data has  208  samples and  172  features will be tested!
## Imputation approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results_genus[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results_genus[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# summary statistics
# raw_linda_results_genus <- binomial_statistics(filt_ileum_uni_data,             
#                                             group=group,
#                                             filt_ileum_uni_metadata,
#                                             raw_linda_results_genus,
#                                             segment = "terminal_ileum")

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle("Country effect") 

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_ileum_uni_taxa) +
            ggtitle("Interaction")

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano2 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa,variable="Country") + 
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)

## Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text_repel()`).

## Warning: Removed 8 rows containing missing values or values outside the scale range (`geom_text_repel()`).

volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_genus, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_ileum_uni_data,
                                          filt_ileum_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_genus[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "pre_ltx"
## [1] "post_ltx"
## [1] "pre_ltx"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- comparison_name
addWorksheet(wb, sheetName = new_name)
writeData(wb, sheet = new_name, uni_df, rowNames=FALSE)

post_ltx vs healthy

group <- c("healthy","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_genus_tab,
                            ileum_genus_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 45 ASV(s)

## Removing 2 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group * Country')

## 0  features are filtered!
## The filtered data has  211  samples and  180  features will be tested!
## Imputation approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results_genus[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results_genus[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# summary statistics
# raw_linda_results_genus <- binomial_statistics(filt_ileum_uni_data,             
#                                             group=group,
#                                             filt_ileum_uni_metadata,
#                                             raw_linda_results_genus,
#                                             segment = "terminal_ileum")

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle("Country effect") 

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_ileum_uni_taxa) +
            ggtitle("Interaction")

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano2 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa,variable="Country") + 
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)

## Warning: Removed 34 rows containing missing values or values outside the scale range (`geom_text_repel()`).

## Warning: Removed 18 rows containing missing values or values outside the scale range (`geom_text_repel()`).

volcano

## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider increasing max.overlaps

## Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_genus, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_ileum_uni_data,
                                          filt_ileum_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_genus[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "post_ltx"
## [1] "healthy"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- comparison_name
addWorksheet(wb, sheetName = new_name)
writeData(wb, sheet = new_name, uni_df, rowNames=FALSE)

Visualization

Heatmap visualizing the linDA’s logFoldChange for taxa with p < 0.1.

list_heatmap <- list_intersections[grep(paste(segment,level),
                                  names(list_intersections),value=TRUE)]

p_heatmap_linda <- heatmap_linda(list_heatmap,ileum_taxa_tab)
p_heatmap_linda

Dot heatmap

dotheatmap_linda <- dot_heatmap_linda(list_heatmap,
                                      uni_df,
                                      ileum_taxa_tab)

## min_clr -1.767585 
## max_clr 6.879926 
## min_log -5.062147 
## max_log 4.892498

dotheatmap_linda

PSC effect

pre_LTx vs Healthy and Post_LTx vs Healthy intersection

imagename

A <- list_intersections[[paste(segment,level,"healthy vs pre_ltx")]]$SeqID
B <- list_intersections[[paste(segment,level,"healthy vs post_ltx")]]$SeqID
C <- intersect(A,B)

psc_effect[[paste(segment,level)]] <- list_intersections[[paste(segment,level, "healthy vs pre_ltx")]][list_intersections[[paste(segment,level, "healthy vs pre_ltx")]]$SeqID %in% C,]
  
# see the results
psc_effect[[paste(segment,level)]] 

Saving results

# ALL DATA
saveWorkbook(wb,file.path(path,paste0("uni_analysis_wb_",segment,".xlsx")),
             overwrite = TRUE)

# PSC effect
write.xlsx(psc_effect[[paste(segment,level)]],file.path(path,paste0("psc_effect_",segment,".xlsx")))

# SIGNIFICANT taxa

write.xlsx(list_intersections[grepl(segment,names(list_intersections))] %>%
            `names<-`(gsub(segment, "", names(
              list_intersections[grepl(segment,names(list_intersections))]))),
           file.path(path,paste0("significant_taxa_",segment,".xlsx")))

Supplementary Analysis

supplements_uni <- list()
supplements_wb <- createWorkbook()

ASV level

level="ASV"

path_maaslin="../intermediate_files/maaslin/Q1/ASV/"

raw_linda_results <- list();
raw_linda_results[[segment]] <- list()
linda_results <- list(); 
linda_results[[segment]] <- list()

pre_ltx vs healthy

group <- c("healthy","pre_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_asv_tab,
                            ileum_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 1598 ASV(s)

## Removing 146 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group * Country')

## 0  features are filtered!
## The filtered data has  143  samples and  453  features will be tested!
## Imputation approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle("Country effect") 

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_ileum_uni_taxa) +
            ggtitle("Interaction")

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano2 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa,variable="Country") + 
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)
volcano

## Warning: ggrepel: 97 unlabeled data points (too many overlaps). Consider increasing max.overlaps

## Warning: ggrepel: 36 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_ileum_uni_data,
                                          filt_ileum_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "pre_ltx"
## [1] "healthy"
## [1] "pre_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_asv_tab,
                            ileum_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 979 ASV(s)

## Removing 68 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group * Country')

## 0  features are filtered!
## The filtered data has  208  samples and  349  features will be tested!
## Imputation approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# summary statistics
# raw_linda_results_genus <- binomial_statistics(filt_ileum_uni_data,             
#                                             group=group,
#                                             filt_ileum_uni_metadata,
#                                             raw_linda_results_genus,
#                                             segment = "terminal_ileum")

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle("Country effect") 

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_ileum_uni_taxa) +
            ggtitle("Interaction")

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano2 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa,variable="Country") + 
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_ileum_uni_data,
                                          filt_ileum_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "pre_ltx"
## [1] "post_ltx"
## [1] "pre_ltx"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

post_ltx vs healthy

group <- c("healthy","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_asv_tab,
                            ileum_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 641 ASV(s)

## Removing 104 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group * Country')

## 0  features are filtered!
## The filtered data has  211  samples and  444  features will be tested!
## Pseudo-count approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# summary statistics
# raw_linda_results_genus <- binomial_statistics(filt_ileum_uni_data,             
#                                             group=group,
#                                             filt_ileum_uni_metadata,
#                                             raw_linda_results_genus,
#                                             segment = "terminal_ileum")

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle("Country effect") 

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_ileum_uni_taxa) +
            ggtitle("Interaction")

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano2 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa,variable="Country") + 
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)
volcano

## Warning: ggrepel: 136 unlabeled data points (too many overlaps). Consider increasing max.overlaps

## Warning: ggrepel: 38 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_ileum_uni_data,
                                          filt_ileum_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "post_ltx"
## [1] "healthy"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

Visualization

Heatmap visualizing the linDA’s logFoldChange for taxa with p < 0.1.

list_heatmap <- list_intersections[grep(paste(segment,level),
                                  names(list_intersections),value=TRUE)]

p_heatmap_linda <- heatmap_linda(list_heatmap,ileum_taxa_tab)
p_heatmap_linda

Dot heatmap

dotheatmap_linda <- dot_heatmap_linda(list_heatmap,
                                      uni_df,
                                      ileum_taxa_tab)

## min_clr -0.9898658 
## max_clr 5.129882 
## min_log -5.165415 
## max_log 4.224397

dotheatmap_linda

PSC effect

pre_LTx vs Healthy and Post_LTx vs Healthy intersection

imagename

A <- list_intersections[[paste(segment,level,"healthy vs pre_ltx")]]$SeqID
B <- list_intersections[[paste(segment,level,"healthy vs post_ltx")]]$SeqID
C <- intersect(A,B)

psc_effect[[paste(segment,level)]] <- list_intersections[[paste(segment,level, "healthy vs pre_ltx")]][list_intersections[[paste(segment,level, "healthy vs pre_ltx")]]$SeqID %in% C,]
  
# see the results
psc_effect[[paste(segment,level)]] 

Phylum level

level="phylum"

path_maaslin="../intermediate_files/maaslin/Q1/Phylum/"

raw_linda_results_phylum <- list();
raw_linda_results_phylum[[segment]] <- list()
linda_results_phylum <- list(); 
linda_results_phylum[[segment]] <- list()

Aggregate taxa

phylum_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = "Phylum")

ileum_phylum_tab <- phylum_data[[1]]
ileum_phylum_taxa_tab <- phylum_data[[2]]

pre_ltx vs healthy

group <- c("healthy","pre_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_phylum_tab,
                            ileum_phylum_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 2 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group * Country')

## 0  features are filtered!
## The filtered data has  143  samples and  10  features will be tested!
## Imputation approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results_phylum[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results_phylum[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

## Using Phylum for naming

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle("Country effect") 

## Using Phylum for naming

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_ileum_uni_taxa) +
            ggtitle("Interaction")

## Using Phylum for naming

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano2 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa,variable="Country") + 
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_phylum, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_ileum_uni_data,
                                          filt_ileum_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_phylum[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "pre_ltx"
## [1] "healthy"
## [1] "pre_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_phylum_tab,
                            ileum_phylum_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 2 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group * Country')

## 0  features are filtered!
## The filtered data has  208  samples and  10  features will be tested!
## Imputation approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results_phylum[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results_phylum[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# summary statistics
# raw_linda_results_genus <- binomial_statistics(filt_ileum_uni_data,             
#                                             group=group,
#                                             filt_ileum_uni_metadata,
#                                             raw_linda_results_genus,
#                                             segment = "terminal_ileum")

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

## Using Phylum for naming

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle("Country effect") 

## Using Phylum for naming

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_ileum_uni_taxa) +
            ggtitle("Interaction")

## Using Phylum for naming

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)
volcano

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano2 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa,variable="Country") + 
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_phylum, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_ileum_uni_data,
                                          filt_ileum_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_phylum[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "pre_ltx"
## [1] "post_ltx"
## [1] "pre_ltx"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

post_ltx vs healthy

group <- c("healthy","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_phylum_tab,
                            ileum_phylum_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 1 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group * Country')

## 0  features are filtered!
## The filtered data has  211  samples and  10  features will be tested!
## Imputation approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results_phylum[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results_phylum[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# summary statistics
# raw_linda_results_genus <- binomial_statistics(filt_ileum_uni_data,             
#                                             group=group,
#                                             filt_ileum_uni_metadata,
#                                             raw_linda_results_genus,
#                                             segment = "terminal_ileum")

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

## Using Phylum for naming

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle("Country effect") 

## Using Phylum for naming

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_ileum_uni_taxa) +
            ggtitle("Interaction")

## Using Phylum for naming

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano2 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa,variable="Country") + 
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_phylum, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_ileum_uni_data,
                                          filt_ileum_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_phylum[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "post_ltx"
## [1] "healthy"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

Visualization

Heatmap visualizing the linDA’s logFoldChange for taxa with p < 0.1.

list_heatmap <- list_intersections[grep(paste(segment,level),
                                  names(list_intersections),value=TRUE)]

p_heatmap_linda <- heatmap_linda(list_heatmap,ileum_taxa_tab)
p_heatmap_linda

Dot heatmap

dotheatmap_linda <- dot_heatmap_linda(list_heatmap,
                                      uni_df,
                                      ileum_taxa_tab)

## min_clr -4.119052 
## max_clr 3.1473 
## min_log -2.944293 
## max_log 2.82227

dotheatmap_linda

PSC effect

pre_LTx vs Healthy and Post_LTx vs Healthy intersection

imagename

A <- list_intersections[[paste(segment,level,"healthy vs pre_ltx")]]$SeqID
B <- list_intersections[[paste(segment,level,"healthy vs post_ltx")]]$SeqID
C <- intersect(A,B)

psc_effect[[paste(segment,level)]] <- list_intersections[[paste(segment,level, "healthy vs pre_ltx")]][list_intersections[[paste(segment,level, "healthy vs pre_ltx")]]$SeqID %in% C,]
  
# see the results
psc_effect[[paste(segment,level)]] 

Saving results

# ALL DATA
saveWorkbook(supplements_wb,file.path(path,paste0("supplements_uni_analysis_wb_",segment,".xlsx")),overwrite = TRUE)

# PSC effect
write.xlsx(psc_effect,
          file.path(path,paste0("supplements_psc_effect_",segment,".csv")))

# SIGNIFICANT taxa
write.xlsx(list_intersections[grepl(segment,names(list_intersections))] %>%
            `names<-`(gsub(segment, "", names(
              list_intersections[grepl(segment,names(list_intersections))]))),
           file.path(path,paste0("supplements_significant_taxa_",segment,".xlsx")))

Machine learning

path = "../results/Q1/models"

ElasticNet

model="enet"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 1598 ASV(s)

## Removing 146 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ <- list()
models_cm <- list()
betas <- list()
roc_cs <- list()

models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                      [,1]
## alpha                           0.4000000
## lambda                          0.0256910
## auc                             1.0000000
## auc_czech                       1.0000000
## auc_no                          1.0000000
## auc_optimism_corrected          0.9565915
## auc_optimism_corrected_CIL      0.9256846
## auc_optimism_corrected_CIU      0.9902260
## accuracy                        1.0000000
## accuracy_czech                        NaN
## accuracy_no                     1.0000000
## accuracy_optimism_corrected     0.8862274
## accuracy_optimism_corrected_CIL 0.8365012
## accuracy_optimism_corrected_CIU 0.9385771

enet_model$conf_matrices

## $original
##     Predicted
## True  0  1
##    0 73  0
##    1  0 70
## 
## $czech
##     Predicted
## True  0  1
##    0 37  0
##    1  0 24
## 
## $no
##     Predicted
## True  0  1
##    0 36  0
##    1  0 46

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 979 ASV(s)

## Removing 68 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                      [,1]
## alpha                           0.2000000
## lambda                          0.1292328
## auc                             0.9967909
## auc_czech                       0.9979575
## auc_no                          0.9969807
## auc_optimism_corrected          0.8737118
## auc_optimism_corrected_CIL      0.8172808
## auc_optimism_corrected_CIU      0.9228860
## accuracy                        0.9278846
## accuracy_czech                        NaN
## accuracy_no                     0.9512195
## accuracy_optimism_corrected     0.8320429
## accuracy_optimism_corrected_CIL 0.7800169
## accuracy_optimism_corrected_CIU 0.8854102

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0 138   0
##    1  15  55
## 
## $czech
##     Predicted
## True   0   1
##    0 102   0
##    1  11  13
## 
## $no
##     Predicted
## True  0  1
##    0 36  0
##    1  4 42

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 641 ASV(s)

## Removing 104 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.20000000
## lambda                          0.04799701
## auc                             1.00000000
## auc_czech                       1.00000000
## auc_no                          1.00000000
## auc_optimism_corrected          0.95028295
## auc_optimism_corrected_CIL      0.90130423
## auc_optimism_corrected_CIU      0.97057023
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                     1.00000000
## accuracy_optimism_corrected     0.88050916
## accuracy_optimism_corrected_CIL 0.82402597
## accuracy_optimism_corrected_CIU 0.92772937

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0  73   0
##    1   0 138
## 
## $czech
##     Predicted
## True   0   1
##    0  37   0
##    1   0 102
## 
## $no
##     Predicted
## True  0  1
##    0 36  0
##    1  0 36

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 84 ASV(s)

## Removing 10 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.20000000
## lambda                          0.03984448
## auc                             1.00000000
## auc_czech                       1.00000000
## auc_no                          1.00000000
## auc_optimism_corrected          0.97676412
## auc_optimism_corrected_CIL      0.96031633
## auc_optimism_corrected_CIU      0.99472299
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                     1.00000000
## accuracy_optimism_corrected     0.92043601
## accuracy_optimism_corrected_CIL 0.88214286
## accuracy_optimism_corrected_CIU 0.94598214

enet_model$conf_matrices

## $original
##     Predicted
## True  0  1
##    0 73  0
##    1  0 70
## 
## $czech
##     Predicted
## True  0  1
##    0 37  0
##    1  0 24
## 
## $no
##     Predicted
## True  0  1
##    0 36  0
##    1  0 46

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 46 ASV(s)

## Removing 6 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                      [,1]
## alpha                           0.0000000
## lambda                          0.6674012
## auc                             0.9726708
## auc_czech                       0.9616013
## auc_no                          0.9800725
## auc_optimism_corrected          0.8411210
## auc_optimism_corrected_CIL      0.7648016
## auc_optimism_corrected_CIU      0.8953735
## accuracy                        0.8653846
## accuracy_czech                        NaN
## accuracy_no                     0.8780488
## accuracy_optimism_corrected     0.7894889
## accuracy_optimism_corrected_CIL 0.7247838
## accuracy_optimism_corrected_CIU 0.8527640

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0 136   2
##    1  26  44
## 
## $czech
##     Predicted
## True   0   1
##    0 101   1
##    1  17   7
## 
## $no
##     Predicted
## True  0  1
##    0 35  1
##    1  9 37

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,group, 
                                     usage="ml_clr")

## Removing 45 ASV(s)

## Removing 2 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.00000000
## lambda                          0.07097722
## auc                             1.00000000
## auc_czech                       1.00000000
## auc_no                          1.00000000
## auc_optimism_corrected          0.94538506
## auc_optimism_corrected_CIL      0.92399418
## auc_optimism_corrected_CIU      0.96914530
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                     1.00000000
## accuracy_optimism_corrected     0.87547363
## accuracy_optimism_corrected_CIL 0.84179739
## accuracy_optimism_corrected_CIU 0.90021493

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0  73   0
##    1   0 138
## 
## $czech
##     Predicted
## True   0   1
##    0  37   0
##    1   0 102
## 
## $no
##     Predicted
## True  0  1
##    0 36  0
##    1  0 36

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

Saving results

models_summ_df_ileum <- do.call(rbind, 
  models_summ[grep(segment,names(models_summ),value = TRUE)])

write.csv(models_summ_df_ileum,file.path(path,paste0("elastic_net_",segment,".csv")))

Supplementary models

supplements_models <- list()

CLR-transformed data

kNN

model="knn"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 1598 ASV(s)

## Removing 146 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               18.0000000
## auc                              0.9239726
## auc_optimism_corrected           0.8629524
## auc_optimism_corrected_CIL       0.7749129
## auc_optimism_corrected_CIU       0.9455801
## accuracy                         0.7762238
## accuracy_optimism_corrected      0.7653048
## accuracy_optimism_corrected_CIL  0.6675617
## accuracy_optimism_corrected_CIU  0.8620370

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)


# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 979 ASV(s)

## Removing 68 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               16.0000000
## auc                              0.8342650
## auc_optimism_corrected           0.6760406
## auc_optimism_corrected_CIL       0.5941213
## auc_optimism_corrected_CIU       0.7720387
## accuracy                         0.7067308
## accuracy_optimism_corrected      0.6673408
## accuracy_optimism_corrected_CIL  0.5938816
## accuracy_optimism_corrected_CIU  0.7531892

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 641 ASV(s)

## Removing 104 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               27.0000000
## auc                              0.9100159
## auc_optimism_corrected           0.8524855
## auc_optimism_corrected_CIL       0.8202755
## auc_optimism_corrected_CIU       0.8956982
## accuracy                         0.8720379
## accuracy_optimism_corrected      0.8053155
## accuracy_optimism_corrected_CIL  0.7275370
## accuracy_optimism_corrected_CIU  0.8627045

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 84 ASV(s)

## Removing 10 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               12.0000000
## auc                              0.9568493
## auc_optimism_corrected           0.8908188
## auc_optimism_corrected_CIL       0.8216601
## auc_optimism_corrected_CIU       0.9482413
## accuracy                         0.7692308
## accuracy_optimism_corrected      0.7759465
## accuracy_optimism_corrected_CIL  0.7280594
## accuracy_optimism_corrected_CIU  0.8326977

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 46 ASV(s)

## Removing 6 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               17.0000000
## auc                              0.8493789
## auc_optimism_corrected           0.7697624
## auc_optimism_corrected_CIL       0.6675672
## auc_optimism_corrected_CIU       0.8544948
## accuracy                         0.7692308
## accuracy_optimism_corrected      0.7268539
## accuracy_optimism_corrected_CIL  0.6193304
## accuracy_optimism_corrected_CIU  0.8090169

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 45 ASV(s)

## Removing 2 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               12.0000000
## auc                              0.9638674
## auc_optimism_corrected           0.9040111
## auc_optimism_corrected_CIL       0.8471376
## auc_optimism_corrected_CIU       0.9291762
## accuracy                         0.8530806
## accuracy_optimism_corrected      0.7872385
## accuracy_optimism_corrected_CIL  0.6880658
## accuracy_optimism_corrected_CIU  0.8647353

roc_c

Random Forest

model="rf"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 1598 ASV(s)

## Removing 146 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "105"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9326791"
## auc_optimism_corrected_CIL      "0.8868633"
## auc_optimism_corrected_CIU      "0.97808"  
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8586242"
## accuracy_optimism_corrected_CIL "0.7485639"
## accuracy_optimism_corrected_CIU "0.9449821"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 979 ASV(s)

## Removing 68 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "115"      
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.8709091"
## auc_optimism_corrected_CIL      "0.8297355"
## auc_optimism_corrected_CIU      "0.9232318"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.848049" 
## accuracy_optimism_corrected_CIL "0.785"    
## accuracy_optimism_corrected_CIU "0.8948743"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 641 ASV(s)

## Removing 104 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "115"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.8879264"
## auc_optimism_corrected_CIL      "0.8392379"
## auc_optimism_corrected_CIU      "0.9540128"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.866501" 
## accuracy_optimism_corrected_CIL "0.8081169"
## accuracy_optimism_corrected_CIU "0.9166327"

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 84 ASV(s)

## Removing 10 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "47"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.9388728"
## auc_optimism_corrected_CIL      "0.8966991"
## auc_optimism_corrected_CIU      "0.9860245"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8715614"
## accuracy_optimism_corrected_CIL "0.7878848"
## accuracy_optimism_corrected_CIU "0.9405808"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 46 ASV(s)

## Removing 6 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "57"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.8289652"
## auc_optimism_corrected_CIL      "0.7758374"
## auc_optimism_corrected_CIU      "0.8725973"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.7853797"
## accuracy_optimism_corrected_CIL "0.7165954"
## accuracy_optimism_corrected_CIU "0.8361266"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 45 ASV(s)

## Removing 2 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "133"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.8871871"
## auc_optimism_corrected_CIL      "0.8400874"
## auc_optimism_corrected_CIU      "0.9319979"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8436258"
## accuracy_optimism_corrected_CIL "0.7777262"
## accuracy_optimism_corrected_CIU "0.9010219"

roc_c

Gradient boosting

model="gb"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 1598 ASV(s)

## Removing 146 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 1.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   20.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9519731
## auc_optimism_corrected_CIL        0.9242466
## auc_optimism_corrected_CIU        0.9792948
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.8698671
## accuracy_optimism_corrected_CIL   0.8187108
## accuracy_optimism_corrected_CIU   0.9242252

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 979 ASV(s)

## Removing 68 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs


# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         100.0000000
## interaction.depth                 5.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   20.0000000
## auc                               0.9998965
## auc_optimism_corrected            0.8718248
## auc_optimism_corrected_CIL        0.8295510
## auc_optimism_corrected_CIU        0.9205059
## accuracy                          0.9903846
## accuracy_optimism_corrected       0.8469315
## accuracy_optimism_corrected_CIL   0.7827679
## accuracy_optimism_corrected_CIU   0.9174550

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 641 ASV(s)

## Removing 104 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs


# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         100.0000000
## interaction.depth                 5.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   20.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9043613
## auc_optimism_corrected_CIL        0.8254146
## auc_optimism_corrected_CIU        0.9448907
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.8611614
## accuracy_optimism_corrected_CIL   0.8101371
## accuracy_optimism_corrected_CIU   0.9030050

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 84 ASV(s)

## Removing 10 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 1.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   20.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9716910
## auc_optimism_corrected_CIL        0.9485261
## auc_optimism_corrected_CIU        0.9939384
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.9036238
## accuracy_optimism_corrected_CIL   0.8464694
## accuracy_optimism_corrected_CIU   0.9439518

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 46 ASV(s)

## Removing 6 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         100.0000000
## interaction.depth                 1.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   10.0000000
## auc                               0.9909938
## auc_optimism_corrected            0.8247321
## auc_optimism_corrected_CIL        0.7627228
## auc_optimism_corrected_CIU        0.8722840
## accuracy                          0.9278846
## accuracy_optimism_corrected       0.7958716
## accuracy_optimism_corrected_CIL   0.6784211
## accuracy_optimism_corrected_CIU   0.8512684

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 45 ASV(s)

## Removing 2 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         200.0000000
## interaction.depth                 3.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   20.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9205235
## auc_optimism_corrected_CIL        0.8833925
## auc_optimism_corrected_CIU        0.9661175
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.8677391
## accuracy_optimism_corrected_CIL   0.8267488
## accuracy_optimism_corrected_CIU   0.9039825

roc_c

Relative abundances

Elastic net

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 1598 ASV(s)

## Removing 146 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           1.00000000
## lambda                          0.06282139
## auc                             0.99726027
## auc_czech                       0.99662162
## auc_no                          0.99818841
## auc_optimism_corrected          0.92410231
## auc_optimism_corrected_CIL      0.83677721
## auc_optimism_corrected_CIU      0.97242424
## accuracy                        0.93706294
## accuracy_czech                         NaN
## accuracy_no                     0.93902439
## accuracy_optimism_corrected     0.85997837
## accuracy_optimism_corrected_CIL 0.77602041
## accuracy_optimism_corrected_CIU 0.91766690

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 979 ASV(s)

## Removing 68 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                      [,1]
## alpha                           0.0000000
## lambda                          3.1074497
## auc                             0.9894410
## auc_czech                       0.9950980
## auc_no                          0.9734300
## auc_optimism_corrected          0.7340394
## auc_optimism_corrected_CIL      0.6170852
## auc_optimism_corrected_CIU      0.8336118
## accuracy                        0.7259615
## accuracy_czech                        NaN
## accuracy_no                     0.5243902
## accuracy_optimism_corrected     0.6974512
## accuracy_optimism_corrected_CIL 0.5967411
## accuracy_optimism_corrected_CIU 0.8050044

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)


# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 641 ASV(s)

## Removing 104 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                      [,1]
## alpha                           0.2000000
## lambda                          0.0941553
## auc                             1.0000000
## auc_czech                       1.0000000
## auc_no                          1.0000000
## auc_optimism_corrected          0.8878147
## auc_optimism_corrected_CIL      0.8182230
## auc_optimism_corrected_CIU      0.9395208
## accuracy                        0.9620853
## accuracy_czech                        NaN
## accuracy_no                     0.9861111
## accuracy_optimism_corrected     0.8346693
## accuracy_optimism_corrected_CIL 0.7516067
## accuracy_optimism_corrected_CIU 0.9093405

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 84 ASV(s)

## Removing 10 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                      [,1]
## alpha                           0.2000000
## lambda                          0.1357028
## auc                             0.9982387
## auc_czech                       0.9954955
## auc_no                          1.0000000
## auc_optimism_corrected          0.9386177
## auc_optimism_corrected_CIL      0.8965000
## auc_optimism_corrected_CIU      0.9793945
## accuracy                        0.9860140
## accuracy_czech                        NaN
## accuracy_no                     1.0000000
## accuracy_optimism_corrected     0.8663045
## accuracy_optimism_corrected_CIL 0.8016509
## accuracy_optimism_corrected_CIU 0.9358844

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)


# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 46 ASV(s)

## Removing 6 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.40000000
## lambda                          0.08652139
## auc                             0.94720497
## auc_czech                       0.95751634
## auc_no                          0.88828502
## auc_optimism_corrected          0.73722094
## auc_optimism_corrected_CIL      0.65355232
## auc_optimism_corrected_CIU      0.81212375
## accuracy                        0.82211538
## accuracy_czech                         NaN
## accuracy_no                     0.73170732
## accuracy_optimism_corrected     0.71869940
## accuracy_optimism_corrected_CIL 0.61986111
## accuracy_optimism_corrected_CIU 0.79628473

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 45 ASV(s)

## Removing 2 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                      [,1]
## alpha                           0.2000000
## lambda                          0.1352860
## auc                             0.9857058
## auc_czech                       0.9809221
## auc_no                          0.9915123
## auc_optimism_corrected          0.8571252
## auc_optimism_corrected_CIL      0.8014650
## auc_optimism_corrected_CIU      0.8939396
## accuracy                        0.9099526
## accuracy_czech                        NaN
## accuracy_no                     0.9583333
## accuracy_optimism_corrected     0.7924925
## accuracy_optimism_corrected_CIL 0.7205995
## accuracy_optimism_corrected_CIU 0.8558642

roc_c

kNN

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 1598 ASV(s)

## Removing 146 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.9155577
## auc_optimism_corrected           0.8288216
## auc_optimism_corrected_CIL       0.6990440
## auc_optimism_corrected_CIU       0.9324125
## accuracy                         0.6993007
## accuracy_optimism_corrected      0.6665810
## accuracy_optimism_corrected_CIL  0.5590566
## accuracy_optimism_corrected_CIU  0.7549259

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 979 ASV(s)

## Removing 68 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               20.0000000
## auc                              0.8137164
## auc_optimism_corrected           0.6656270
## auc_optimism_corrected_CIL       0.5828798
## auc_optimism_corrected_CIU       0.7357973
## accuracy                         0.6778846
## accuracy_optimism_corrected      0.6814455
## accuracy_optimism_corrected_CIL  0.6193304
## accuracy_optimism_corrected_CIU  0.7416264

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 641 ASV(s)

## Removing 104 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               15.0000000
## auc                              0.8773576
## auc_optimism_corrected           0.8241731
## auc_optimism_corrected_CIL       0.7426389
## auc_optimism_corrected_CIU       0.9093236
## accuracy                         0.8578199
## accuracy_optimism_corrected      0.7672256
## accuracy_optimism_corrected_CIL  0.6851333
## accuracy_optimism_corrected_CIU  0.8682099

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 84 ASV(s)

## Removing 10 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               11.0000000
## auc                              0.9076321
## auc_optimism_corrected           0.8187868
## auc_optimism_corrected_CIL       0.7649740
## auc_optimism_corrected_CIU       0.8737386
## accuracy                         0.7622378
## accuracy_optimism_corrected      0.7045763
## accuracy_optimism_corrected_CIL  0.6121154
## accuracy_optimism_corrected_CIU  0.7918367

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 46 ASV(s)

## Removing 6 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               19.0000000
## auc                              0.8088509
## auc_optimism_corrected           0.7182619
## auc_optimism_corrected_CIL       0.6604457
## auc_optimism_corrected_CIU       0.7917652
## accuracy                         0.7403846
## accuracy_optimism_corrected      0.7189495
## accuracy_optimism_corrected_CIL  0.6446429
## accuracy_optimism_corrected_CIU  0.7566076

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 45 ASV(s)

## Removing 2 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               21.0000000
## auc                              0.8874330
## auc_optimism_corrected           0.8009876
## auc_optimism_corrected_CIL       0.7335368
## auc_optimism_corrected_CIU       0.8864556
## accuracy                         0.8151659
## accuracy_optimism_corrected      0.7465345
## accuracy_optimism_corrected_CIL  0.6166738
## accuracy_optimism_corrected_CIU  0.8089339

roc_c

Random Forest

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 1598 ASV(s)

## Removing 146 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "71"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.9749018"
## auc_optimism_corrected_CIL      "0.9501443"
## auc_optimism_corrected_CIU      "0.9929425"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8954299"
## accuracy_optimism_corrected_CIL "0.8438571"
## accuracy_optimism_corrected_CIU "0.930791"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 979 ASV(s)

## Removing 68 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "31"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.901005" 
## auc_optimism_corrected_CIL      "0.8504941"
## auc_optimism_corrected_CIU      "0.9534551"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8441077"
## accuracy_optimism_corrected_CIL "0.785"    
## accuracy_optimism_corrected_CIU "0.8924805"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 641 ASV(s)

## Removing 104 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "17"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.9248253"
## auc_optimism_corrected_CIL      "0.8775136"
## auc_optimism_corrected_CIU      "0.9641348"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.875495" 
## accuracy_optimism_corrected_CIL "0.8132086"
## accuracy_optimism_corrected_CIU "0.9172763"

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 84 ASV(s)

## Removing 10 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "17"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.9665056"
## auc_optimism_corrected_CIL      "0.9449662"
## auc_optimism_corrected_CIU      "0.9863349"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.902696" 
## accuracy_optimism_corrected_CIL "0.8441384"
## accuracy_optimism_corrected_CIU "0.9399633"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 46 ASV(s)

## Removing 6 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "85"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.8604571"
## auc_optimism_corrected_CIL      "0.824759" 
## auc_optimism_corrected_CIU      "0.9035517"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8070124"
## accuracy_optimism_corrected_CIL "0.758049" 
## accuracy_optimism_corrected_CIU "0.8571429"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 45 ASV(s)

## Removing 2 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "31"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.9158303"
## auc_optimism_corrected_CIL      "0.8472241"
## auc_optimism_corrected_CIU      "0.9820129"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8678365"
## accuracy_optimism_corrected_CIL "0.8040909"
## accuracy_optimism_corrected_CIU "0.9498863"

roc_c

Gradient boosting

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 1598 ASV(s)

## Removing 146 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 3.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   10.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9694065
## auc_optimism_corrected_CIL        0.9465650
## auc_optimism_corrected_CIU        0.9947741
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.9138621
## accuracy_optimism_corrected_CIL   0.8255102
## accuracy_optimism_corrected_CIU   0.9639881

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 979 ASV(s)

## Removing 68 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         200.0000000
## interaction.depth                 3.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   10.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.8818766
## auc_optimism_corrected_CIL        0.8250024
## auc_optimism_corrected_CIU        0.9331889
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.8505813
## accuracy_optimism_corrected_CIL   0.7753125
## accuracy_optimism_corrected_CIU   0.9131829

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 641 ASV(s)

## Removing 104 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         100.0000000
## interaction.depth                 3.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   10.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9152636
## auc_optimism_corrected_CIL        0.8490806
## auc_optimism_corrected_CIU        0.9591980
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.8440457
## accuracy_optimism_corrected_CIL   0.7779221
## accuracy_optimism_corrected_CIU   0.9026667

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 84 ASV(s)

## Removing 10 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         100.0000000
## interaction.depth                 3.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   10.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9704068
## auc_optimism_corrected_CIL        0.9420501
## auc_optimism_corrected_CIU        0.9912599
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.9084848
## accuracy_optimism_corrected_CIL   0.8403249
## accuracy_optimism_corrected_CIU   0.9575208

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 46 ASV(s)

## Removing 6 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 3.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   10.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.8347726
## auc_optimism_corrected_CIL        0.7841428
## auc_optimism_corrected_CIU        0.8892752
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.8039108
## accuracy_optimism_corrected_CIL   0.7626480
## accuracy_optimism_corrected_CIU   0.8542208

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 45 ASV(s)

## Removing 2 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 5.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   10.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9470524
## auc_optimism_corrected_CIL        0.9072132
## auc_optimism_corrected_CIU        0.9829447
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.8996262
## accuracy_optimism_corrected_CIL   0.8474900
## accuracy_optimism_corrected_CIU   0.9373579

roc_c

Saving results

models_list <- list()

for (model_name in names(supplements_models$models_summ)){
  df <- do.call(rbind, supplements_models$models_summ[[model_name]])
  models_list[[model_name]] <- df
}

write.xlsx(models_list,
           file=file.path(path,paste0("supplements_models_",segment,".xlsx")),
           rowNames=TRUE)

Results overview

Alpha diversity

pc_observed[[segment]]

pc_shannon[[segment]]

pc_simpson[[segment]]

pc_pielou[[segment]]

Plots

alpha_div_plots[[paste(segment,"Country")]]

alpha_div_plots[[paste(segment,"Custom")]]

Beta diversity

Main results

pairwise_aitchison_raw[[paste("genus", segment)]]

PCA

pca_plots_list[[paste(segment,"genus custom")]]

Supplements

knitr::kable(supplements_beta[!grepl("PCoA",names(supplements_beta))],
             digits = 3,
             caption = "Supplementary PERMANOVA results")

Supplementary PERMANOVA results

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 0.963 5.058 0.033 0.001 0.002 ** pre_ltx vs post_ltx 1 0.342 1.581 0.007 0.070 0.070 post_ltx vs healthy 1 1.551 7.718 0.034 0.001 0.002 ** pre_ltx vs healthy , Country 1 1.132 5.948 0.039 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 1.991 9.200 0.042 0.001 0.001 *** post_ltx vs healthy , Country 1 1.656 8.242 0.037 0.001 0.001 *** pre_ltx vs healthy : Country 1 0.449 2.380 0.016 0.002 0.006 ** pre_ltx vs post_ltx : Country 1 0.330 1.527 0.007 0.075 0.075 post_ltx vs healthy : Country 1 0.376 1.878 0.008 0.013 0.020 *

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 1.087 3.929 0.026 0.001 0.002 ** pre_ltx vs post_ltx 1 0.439 1.457 0.007 0.045 0.045 * post_ltx vs healthy 1 1.633 5.675 0.026 0.001 0.002 ** pre_ltx vs healthy , Country 1 1.232 4.452 0.030 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 1.965 6.513 0.030 0.001 0.001 *** post_ltx vs healthy , Country 1 1.751 6.085 0.028 0.001 0.001 *** pre_ltx vs healthy : Country 1 0.525 1.912 0.013 0.002 0.006 ** pre_ltx vs post_ltx : Country 1 0.405 1.346 0.006 0.072 0.072 post_ltx vs healthy : Country 1 0.450 1.569 0.007 0.017 0.026 *

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 1010.000 3.749 0.026 0.001 0.002 ** pre_ltx vs post_ltx 1 364.559 1.485 0.007 0.003 0.003 ** post_ltx vs healthy 1 1389.311 5.466 0.025 0.001 0.002 ** pre_ltx vs healthy , Country 1 603.397 2.239 0.015 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 671.339 2.734 0.013 0.001 0.001 *** post_ltx vs healthy , Country 1 789.105 3.105 0.014 0.001 0.001 *** pre_ltx vs healthy : Country 1 364.013 1.354 0.009 0.014 0.021 * pre_ltx vs post_ltx : Country 1 352.491 1.439 0.007 0.007 0.021 * post_ltx vs healthy : Country 1 281.750 1.109 0.005 0.170 0.170

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 1.628 5.225 0.035 0.001 0.002 ** pre_ltx vs post_ltx 1 0.519 1.563 0.007 0.024 0.024 * post_ltx vs healthy 1 2.428 7.667 0.035 0.001 0.002 ** pre_ltx vs healthy , Country 1 1.136 3.644 0.024 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 1.676 5.049 0.024 0.001 0.001 *** post_ltx vs healthy , Country 1 1.667 5.264 0.024 0.001 0.001 *** pre_ltx vs healthy : Country 1 0.469 1.511 0.010 0.027 0.040 * pre_ltx vs post_ltx : Country 1 0.562 1.698 0.008 0.010 0.030 * post_ltx vs healthy : Country 1 0.432 1.367 0.006 0.064 0.064

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 1.354 3.531 0.024 0.001 0.002 ** pre_ltx vs post_ltx 1 0.528 1.327 0.006 0.024 0.024 * post_ltx vs healthy 1 1.921 4.964 0.023 0.001 0.002 ** pre_ltx vs healthy , Country 1 0.994 2.593 0.018 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 1.384 3.480 0.017 0.001 0.001 *** post_ltx vs healthy , Country 1 1.396 3.607 0.017 0.001 0.001 *** pre_ltx vs healthy : Country 1 0.514 1.344 0.009 0.020 0.030 * pre_ltx vs post_ltx : Country 1 0.571 1.439 0.007 0.010 0.030 * post_ltx vs healthy : Country 1 0.488 1.262 0.006 0.041 0.041 *

PCA

ggarrange(plotlist = supplements_beta[grepl("PCoA",names(supplements_beta))],
          labels=names(supplements_beta[grepl("PCoA",names(supplements_beta))]),
          font.label = list(size=5,face="plain"),
          ncol=2,nrow=3)

Univariate analysis

Number of significant taxa

knitr::kable(cbind(as.data.frame(lapply(list_intersections,nrow)),
      as.data.frame(lapply(psc_effect,nrow))) %>% t() %>% 
  `colnames<-`("Count") %>% 
  `rownames<-`(c(names(list_intersections),"PSC effect ASV","PSC effect Genus","PSC effect Phylum")),caption="Number of significant taxa")

Number of significant taxa

	Count
terminal_ileum genus healthy vs pre_ltx	25
terminal_ileum genus pre_ltx vs post_ltx	4
terminal_ileum genus healthy vs post_ltx	38
terminal_ileum ASV healthy vs pre_ltx	42
terminal_ileum ASV pre_ltx vs post_ltx	2
terminal_ileum ASV healthy vs post_ltx	65
terminal_ileum phylum healthy vs pre_ltx	1
terminal_ileum phylum pre_ltx vs post_ltx	0
terminal_ileum phylum healthy vs post_ltx	3
PSC effect ASV	15
PSC effect Genus	25
PSC effect Phylum	0

Counts

# univar_list <- univariate_statistics(list_intersections,
#                                      psc_effect,
#                                      ileum_genus_asv_taxa_tab)
# 
# univar_df <- univar_list[[1]]
# wb <- univar_list[[2]]
# 
# # save the results
# saveWorkbook(wb,"results/Q1/DAA_final_terminal_ileum.xlsx", overwrite = TRUE)
# 
# # see the results
# univar_df

Machine learning

Main models

Summary

knitr::kable(models_summ_df_ileum %>% dplyr::select(
"alpha","lambda",
"auc_optimism_corrected",
"auc_optimism_corrected_CIL",
"auc_optimism_corrected_CIU"),
             digits=2,caption="Elastic net results")

Elastic net results

	alpha	lambda	auc_optimism_corrected	auc_optimism_corrected_CIL	auc_optimism_corrected_CIU
pre_ltx vs healthy ASV terminal_ileum	0.4	0.03	0.96	0.93	0.99
pre_ltx vs post_ltx ASV terminal_ileum	0.2	0.13	0.87	0.82	0.92
post_ltx vs healthy ASV terminal_ileum	0.2	0.05	0.95	0.90	0.97
pre_ltx vs healthy genus terminal_ileum	0.2	0.04	0.98	0.96	0.99
pre_ltx vs post_ltx genus terminal_ileum	0.0	0.67	0.84	0.76	0.90
post_ltx vs healthy genus terminal_ileum	0.0	0.07	0.95	0.92	0.97

ROC - ASV level

roc_curve_all_custom(roc_cs[c(1:3)], 
                     Q="Q1",
                     model_name="enet_model")

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

ROC - Genus level

roc_curve_all_custom(roc_cs[c(4:6)],Q="Q1",
                     model_name="enet_model")

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

Supplementary models

Summary

# Build final dataframe
models_list[["enet_model"]] <- models_summ_df_ileum
final_df <- tibble(row_names = rownames(models_list[[1]]))

# Loop through models and extract required values
for (model_name in names(models_list)) {
  model_df <- models_list[[model_name]]
  
  # Combine AUC_optimism_corrected with its CI values
  final_df[[model_name]] <- paste0(
    round(model_df$auc_optimism_corrected, 3), 
    " (", round(model_df$auc_optimism_corrected_CIL, 3), "; ", 
    round(model_df$auc_optimism_corrected_CIU, 3), ")"
  )
}

knitr::kable(final_df, caption="All models")

All models

row_names	knn_model	rf_model	gbm_model	enet_model_ra	knn_model_ra	rf_model_ra	gbm_model_ra	enet_model
pre_ltx vs healthy ASV terminal_ileum	0.863 (0.775; 0.946)	0.933 (0.887; 0.978)	0.952 (0.924; 0.979)	0.924 (0.837; 0.972)	0.829 (0.699; 0.932)	0.975 (0.95; 0.993)	0.969 (0.947; 0.995)	0.957 (0.926; 0.99)
pre_ltx vs post_ltx ASV terminal_ileum	0.676 (0.594; 0.772)	0.871 (0.83; 0.923)	0.872 (0.83; 0.921)	0.734 (0.617; 0.834)	0.666 (0.583; 0.736)	0.901 (0.85; 0.953)	0.882 (0.825; 0.933)	0.874 (0.817; 0.923)
post_ltx vs healthy ASV terminal_ileum	0.852 (0.82; 0.896)	0.888 (0.839; 0.954)	0.904 (0.825; 0.945)	0.888 (0.818; 0.94)	0.824 (0.743; 0.909)	0.925 (0.878; 0.964)	0.915 (0.849; 0.959)	0.95 (0.901; 0.971)
pre_ltx vs healthy genus terminal_ileum	0.891 (0.822; 0.948)	0.939 (0.897; 0.986)	0.972 (0.949; 0.994)	0.939 (0.896; 0.979)	0.819 (0.765; 0.874)	0.967 (0.945; 0.986)	0.97 (0.942; 0.991)	0.977 (0.96; 0.995)
pre_ltx vs post_ltx genus terminal_ileum	0.77 (0.668; 0.854)	0.829 (0.776; 0.873)	0.825 (0.763; 0.872)	0.737 (0.654; 0.812)	0.718 (0.66; 0.792)	0.86 (0.825; 0.904)	0.835 (0.784; 0.889)	0.841 (0.765; 0.895)
post_ltx vs healthy genus terminal_ileum	0.904 (0.847; 0.929)	0.887 (0.84; 0.932)	0.921 (0.883; 0.966)	0.857 (0.801; 0.894)	0.801 (0.734; 0.886)	0.916 (0.847; 0.982)	0.947 (0.907; 0.983)	0.945 (0.924; 0.969)

ROC - ASV

rocs_list <- supplements_models$roc_cs
rocs_list[["enet_model"]] <- roc_cs

plot_list <- list()

for (model_name in names(rocs_list)) {
  plot_list[[model_name]] <- roc_curve_all_custom(rocs_list[[model_name]][c(1:3)],
                       Q="Q1",
                       model_name=model_name)
}

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

ggarrange(plotlist = plot_list,labels = names(rocs_list),font.label = list(face="plain",size=7))

ROC - genus

plot_list <- list()

for (model_name in names(rocs_list)) {
  plot_list[[model_name]] <- roc_curve_all_custom(rocs_list[[model_name]][c(4:6)],
                       Q="Q1",
                       model_name=model_name)
}

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

ggarrange(plotlist = plot_list,labels = names(rocs_list),font.label = list(face="plain",size=7))

Analysis - Colon

segment="colon"

Filtering

Rules: - prevalence > 5% (per group) - nearZeroVar with default settings - sequencing depth > 5000 - taxonomic assignment at least order

Rarefaction Curve

path="../intermediate_files/rarecurves"
seq_depth_threshold <- 10000

ps <- construct_phyloseq(colon_asv_tab,colon_taxa_tab,colon_metadata)
rareres <- get_rarecurve(obj=ps, chunks=500)
save(rareres,file = file.path(path,"rarefaction_colon.Rdata"))

load(file.path(path,"rarefaction_colon.Rdata"))
seq_depth_threshold <- 10000
prare <- ggrarecurve(obj=rareres,
                      factorNames="Country",
                      indexNames=c("Observe")) + 
        theme_bw()+
        theme(axis.text=element_text(size=8), 
              panel.grid=element_blank(),
              strip.background = element_rect(colour=NA,fill="grey"),
              strip.text.x = element_text(face="bold")) + 
        geom_vline(xintercept = seq_depth_threshold, 
                   linetype="dashed", color = "red") + 
        xlim(0, 20000)

## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs produced

## The color has been set automatically, you can reset it manually by adding scale_color_manual(values=yourcolors)

prare

## Warning: Removed 22754 rows containing non-finite outside the scale range (`stat_smooth()`).

## Warning: The following aesthetics were dropped during statistical transformation: ymin and ymax.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical variable into a factor?

Library size

read_counts(colon_asv_tab, line = c(5000,10000))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

Sequencing depth

data_filt <- seq_depth_filtering(colon_asv_tab,
                                 colon_taxa_tab,
                                 colon_metadata,
                                 seq_depth_threshold = 10000)

## Removing 75 ASV(s)

filt_colon_asv_tab <- data_filt[[1]]; alpha_colon_asv_tab <- filt_colon_asv_tab
filt_colon_taxa_tab <- data_filt[[2]]; alpha_colon_taxa_tab <- filt_colon_taxa_tab
filt_colon_metadata <- data_filt[[3]]; alpha_colon_metadata <- filt_colon_metadata

seq_step <- dim(filt_colon_asv_tab)[1]

Library size

read_counts(filt_colon_asv_tab,line = c(10000))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

NearZeroVar

data_filt <- nearzerovar_filtering(filt_colon_asv_tab,
                                   filt_colon_taxa_tab,
                                   filt_colon_metadata)

filt_colon_asv_tab <- data_filt[[1]]
filt_colon_taxa_tab <- data_filt[[2]]
nearzero_step <- dim(filt_colon_asv_tab)[1]

Library size

read_counts(filt_colon_asv_tab,line = c(5000,10000))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

Check zero depth

data_filt <- check_zero_depth(filt_colon_asv_tab, 
                              filt_colon_taxa_tab, 
                              filt_colon_metadata)

filt_colon_asv_tab <- data_filt[[1]]; 
filt_colon_taxa_tab <- data_filt[[2]]; 
filt_colon_metadata <- data_filt[[3]]; 

Library size

read_counts(filt_colon_asv_tab,line = c(5000,10000))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

Final Counts

final_counts_filtering(colon_asv_tab,
                       filt_colon_asv_tab,
                       filt_colon_metadata,
                       seq_step, 0, nearzero_step)

Alpha diversity

path = "../results/Q1/alpha_diversity"

Calculation

# Construct MPSE object
alpha_colon_metadata$Sample <- alpha_colon_metadata$SampleID
colon_mpse <- as.MPSE(construct_phyloseq(alpha_colon_asv_tab,
                                         alpha_colon_taxa_tab,
                                         alpha_colon_metadata))

colon_mpse %<>% mp_rrarefy(raresize = 10000,seed = 123)

# Calculate alpha diversity - rarefied counts
colon_mpse %<>% mp_cal_alpha(.abundance=RareAbundance, force=TRUE)

alpha_data <- data.frame(SampleID=colon_mpse$Sample.x,
                         Observe=colon_mpse$Observe,
                         Shannon=colon_mpse$Shannon,
                         Simpson=colon_mpse$Simpson,
                         Pielou=colon_mpse$Pielou,
                         Group=colon_mpse$Group,
                         Country=colon_mpse$Country,
                         Patient=colon_mpse$Patient)

write.csv(alpha_data,file.path(path,paste0("alpha_indices_",segment,".csv")),
          row.names = FALSE)

Plots

Country plot

# preparing data frame

labels = data.frame(country=rep(c("CZ","NO"),3),
                   x=c(0.8,1.19,1.8,2.19,2.8,3.19),
                   y=rep(0.2,6),
                   label=rep(c("CZ","NO"),3))

p_boxplot_alpha <- boxplot_subgroups(alpha_data,labels)

## Using SampleID, Group, Country, Patient as id variables

# save the results
alpha_div_plots[[paste(segment,"Country")]] <- p_boxplot_alpha

# see the results
p_boxplot_alpha

Custom plot

alpha_data <- alpha_data %>% 
  dplyr::select(-c("Simpson","Pielou")) %>%
  mutate(Richness=Observe)

p_B <- alpha_diversity_custom_2(alpha_data,
                                size = 1.5,
                                width = 0.3)

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

# save the results
alpha_div_plots[[paste(segment,"Custom")]] <- p_B

p_B

Linear Model

path = "../results/Q1/alpha_diversity"
alpha_data <- read.csv(file.path(path,paste0("alpha_indices_",segment,".csv")))

Richness

results_model <- pairwise.lmer(
  formula = "Observe ~ Group * Country + (1|Patient)",
  factors=alpha_data$Group,
  data=alpha_data)

# check interaction
if (!is.data.frame(results_model)){
  results_model_observe <- results_model[[1]]
  results_model_observe_detailed <- results_model[[2]]
} else {
  results_model_observe <- results_model
  results_model_observe_detailed <- NA
}

# save the results
pc_observed[[segment]] <- results_model_observe

# see the results
knitr::kable(results_model_observe,digits = 3,
caption = "Raw results of linear model of richness estimation.")

Raw results of linear model of richness estimation.

	Estimate	Std..Error	df	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-56.230	11.738	195.570	-4.791	0.000	0.000	***
healthy vs pre_ltx - CZ vs NO	14.193	10.341	194.538	1.372	0.171	0.256
healthy vs Grouppre_ltx:CountryNO	-7.489	15.169	194.358	-0.494	0.622	0.622
pre_ltx vs Grouppost_ltx	28.886	11.811	267.502	2.446	0.015	0.045	*
pre_ltx vs post_ltx - CZ vs NO	6.742	12.174	265.784	0.554	0.580	0.622
pre_ltx vs Grouppost_ltx:CountryNO	-24.047	15.112	262.828	-1.591	0.113	0.203
healthy vs Grouppost_ltx	-27.299	8.699	250.458	-3.138	0.002	0.009	**
healthy vs post_ltx - CZ vs NO	14.360	11.153	251.729	1.288	0.199	0.256
healthy vs Grouppost_ltx:CountryNO	-31.681	14.195	247.531	-2.232	0.027	0.060

knitr::kable(results_model_observe_detailed,digits = 3,
caption = "Raw results of independent country analysis")

Raw results of independent country analysis

Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 171.780 7.001 162.548 24.535 0.000 NA Grouppost_ltx -27.276 8.558 161.345 -3.187 0.002 0.003 Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 186.110 8.829 88.980 21.080 0 NA Grouppost_ltx -58.943 11.539 87.242 -5.108 0 0 Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 171.793 6.342 89.560 27.090 0.000 NA CountryNO 14.358 9.941 89.408 1.444 0.152 0.152 Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 144.490 5.283 158.786 27.352 0.000 NA CountryNO -17.319 9.272 154.970 -1.868 0.064 0.085

Shannon

results_model <- pairwise.lmer(
  formula = "Shannon ~ Group * Country + (1|Patient)",
  factors=alpha_data$Group,
  data=alpha_data)

# check interaction
if (!is.data.frame(results_model)){
  results_model_shannon <- results_model[[1]]
  results_model_shannon_detailed <- results_model[[2]]
} else {
  results_model_shannon <- results_model
  results_model_shannon_detailed <- NA
}

# save the results
pc_shannon[[segment]] <- as.data.frame(results_model_shannon)

# see the results
knitr::kable(results_model_shannon,digits = 3,
caption = "Raw results of linear model of Shannon estimation.")

Raw results of linear model of Shannon estimation.

	Estimate	Std..Error	df	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-0.585	0.143	197.588	-4.084	0.000	0.001	***
healthy vs pre_ltx - CZ vs NO	0.015	0.126	196.213	0.116	0.907	0.909
healthy vs Grouppre_ltx:CountryNO	0.081	0.185	195.965	0.437	0.663	0.852
pre_ltx vs Grouppost_ltx	0.364	0.158	265.003	2.307	0.022	0.056
pre_ltx vs post_ltx - CZ vs NO	0.095	0.163	263.448	0.585	0.559	0.839
pre_ltx vs Grouppost_ltx:CountryNO	-0.455	0.202	260.765	-2.253	0.025	0.056
healthy vs Grouppost_ltx	-0.220	0.102	247.586	-2.153	0.032	0.058
healthy vs post_ltx - CZ vs NO	0.015	0.131	248.759	0.114	0.909	0.909
healthy vs Grouppost_ltx:CountryNO	-0.376	0.167	244.904	-2.255	0.025	0.056

knitr::kable(results_model_shannon_detailed,digits = 3,
caption = "Raw results of independent country analysis")

Raw results of independent country analysis

Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 3.102 0.144 131.105 21.535 0.000 NA Grouppost_ltx 0.363 0.160 130.742 2.275 0.025 0.049 Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 3.197 0.078 129.531 41.004 0.000 NA Grouppost_ltx -0.091 0.125 129.050 -0.732 0.466 0.563 Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 3.101 0.143 105.863 21.629 0.000 NA CountryNO 0.095 0.164 104.934 0.580 0.563 0.563 Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 3.466 0.068 156.394 51.151 0.000 NA CountryNO -0.360 0.119 153.436 -3.025 0.003 0.012

Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 3.686 0.082 159.084 44.805 0.00 NA Grouppost_ltx -0.220 0.101 158.215 -2.190 0.03 0.04 Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 3.701 0.104 90.164 35.654 0 NA Grouppost_ltx -0.596 0.136 88.069 -4.396 0 0 Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 3.685 0.054 89.090 68.738 0.000 NA CountryNO 0.017 0.084 88.849 0.205 0.838 0.838 Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 3.466 0.068 156.394 51.151 0.000 NA CountryNO -0.360 0.119 153.436 -3.025 0.003 0.006

Simpson

results_model <- pairwise.lmer(
  formula = "Simpson ~ Group * Country + (1|Patient)",
  factors=alpha_data$Group,
  data=alpha_data)

# check interaction
if (!is.data.frame(results_model)){
  results_model_simpson <- results_model[[1]]
  results_model_simpson_detailed <- results_model[[2]]
} else {
  results_model_simpson <- results_model
  results_model_simpson_detailed <- NA
}

# save the results
pc_simpson[[segment]] <- as.data.frame(results_model_simpson)

# see the results
knitr::kable(results_model_simpson,digits = 3,
caption = "Raw results of linear model of Simpson estimation.")

Raw results of linear model of Simpson estimation.

	Estimate	Std..Error	df	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-0.059	0.019	199.853	-3.066	0.002	0.022	*
healthy vs pre_ltx - CZ vs NO	-0.007	0.017	198.127	-0.430	0.667	0.738
healthy vs Grouppre_ltx:CountryNO	0.020	0.025	197.803	0.813	0.417	0.626
pre_ltx vs Grouppost_ltx	0.031	0.026	263.530	1.213	0.226	0.455
pre_ltx vs post_ltx - CZ vs NO	0.013	0.027	261.964	0.476	0.635	0.738
pre_ltx vs Grouppost_ltx:CountryNO	-0.052	0.033	259.262	-1.556	0.121	0.363
healthy vs Grouppost_ltx	-0.027	0.017	246.082	-1.594	0.112	0.363
healthy vs post_ltx - CZ vs NO	-0.007	0.022	247.135	-0.335	0.738	0.738
healthy vs Grouppost_ltx:CountryNO	-0.032	0.028	243.697	-1.147	0.253	0.455

knitr::kable(results_model_simpson_detailed,digits = 3,
caption = "Raw results of independent country analysis")

Table: Raw results of independent country analysis

Pielou

results_model <- pairwise.lmer(
  formula = "Pielou ~ Group * Country + (1|Patient)",
  factors=alpha_data$Group,
  data=alpha_data)

# check interaction
if (!is.data.frame(results_model)){
  results_model_pielou <- results_model[[1]]
  results_model_pielou_detailed <- results_model[[2]]
} else {
  results_model_pielou <- results_model
  results_model_pielou_detailed <- NA
}

# save the results
pc_pielou[[segment]] <- as.data.frame(results_model_pielou)

# see the results
knitr::kable(results_model_pielou,digits = 3,
caption = "Raw results of linear model of Pielou estimation.")

Raw results of linear model of Pielou estimation.

	Estimate	Std..Error	df	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-0.057	0.020	201.130	-2.828	0.005	0.046	*
healthy vs pre_ltx - CZ vs NO	-0.008	0.018	199.017	-0.446	0.656	0.781
healthy vs Grouppre_ltx:CountryNO	0.015	0.026	198.602	0.565	0.573	0.781
pre_ltx vs Grouppost_ltx	0.039	0.024	265.797	1.631	0.104	0.234
pre_ltx vs post_ltx - CZ vs NO	0.007	0.025	263.735	0.274	0.785	0.785
pre_ltx vs Grouppost_ltx:CountryNO	-0.062	0.031	260.213	-2.015	0.045	0.202
healthy vs Grouppost_ltx	-0.018	0.016	247.076	-1.130	0.260	0.467
healthy vs post_ltx - CZ vs NO	-0.008	0.020	248.451	-0.394	0.694	0.781
healthy vs Grouppost_ltx:CountryNO	-0.047	0.026	243.881	-1.828	0.069	0.206

knitr::kable(results_model_pielou_detailed,digits = 3,
caption = "Raw results of independent country analysis")

Raw results of independent country analysis

Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 0.664 0.022 130.842 29.916 0.000 NA Grouppost_ltx 0.039 0.025 130.452 1.582 0.116 0.232 Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 0.670 0.012 129.502 57.528 0.000 NA Grouppost_ltx -0.023 0.019 128.819 -1.219 0.225 0.3 Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 0.664 0.020 107.760 32.578 0.000 NA CountryNO 0.007 0.023 106.251 0.291 0.772 0.772 Estimate Std. Error df t value Pr(>|t|) p.adjusted (Intercept) 0.703 0.011 155.706 65.557 0.000 NA CountryNO -0.055 0.019 152.292 -2.919 0.004 0.016

Saving results

alpha_list <- list(
  Richness=pc_observed[[segment]] %>% rownames_to_column("Comparison"),
  Shannon=pc_shannon[[segment]] %>% rownames_to_column("Comparison"),
  Simpson=pc_simpson[[segment]] %>% rownames_to_column("Comparison"),
  Pielou=pc_pielou[[segment]] %>% rownames_to_column("Comparison"))
                   
write.xlsx(alpha_list, 
           file = file.path(path,paste0("alpha_diversity_results_",segment,".xlsx")))

Beta diversity

Calculating Aitchison distance (euclidean distance on clr-transformed data), both at ASV and genus level.

Main analysis - Genus, Aitchison

Genus level, Aitchison distance

level="genus"

path = "../results/Q1/beta_diversity"

Aggregation, filtering

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level=level,
                             names=TRUE)

filt_data <- filtering_steps(genus_data[[1]],
                             genus_data[[2]],
                             colon_metadata,
                            seq_depth_threshold=10000)

## Removing 10 ASV(s)

filt_colon_genus_tab <- filt_data[[1]]
filt_colon_genus_taxa <- filt_data[[2]]
filt_colon_genus_metadata <- filt_data[[3]]

PERMANOVA

pairwise_df <- filt_colon_genus_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_colon_genus_metadata$Group,
                           covariate = filt_colon_genus_metadata$Country, 
                           patients = filt_colon_genus_metadata$Patient,
                           sim.method = "robust.aitchison", p.adjust.m="BH")

# interaction
pp_int <- pairwise.adonis(pairwise_df,filt_colon_genus_metadata$Group,
                          covariate = filt_colon_genus_metadata$Country, 
                          interaction = TRUE, 
                          patients = filt_colon_genus_metadata$Patient,
                          sim.method = "robust.aitchison", p.adjust.m="BH")

# tidy the results
pp_factor <- pp_main[[1]]
pp_cov <- pp_main[[2]]
pp_fac.cov <- pp_int[[3]]

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols; colnames(pp_cov) <- cols; colnames(pp_fac.cov) <- cols; 

# save raw results
pairwise_aitchison_raw[[paste(level, segment)]] <-rbind(pp_factor,pp_cov,pp_fac.cov)

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	1222.921	7.950	0.019	0.001	0.002	**
pre_ltx vs post_ltx	1	630.146	4.062	0.007	0.008	0.008	**
post_ltx vs healthy	1	1736.169	11.014	0.021	0.001	0.002	**

knitr::kable(pp_cov,digits = 3,caption = "PERMANOVA, COUNTRY separation")

PERMANOVA, COUNTRY separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy , Country	1	738.576	4.801	0.011	0.001	0.001	***
pre_ltx vs post_ltx , Country	1	1532.675	9.879	0.016	0.001	0.001	***
post_ltx vs healthy , Country	1	1728.746	10.966	0.021	0.001	0.001	***

knitr::kable(pp_fac.cov,digits = 3,caption = "PERMANOVA, INTERACTION GROUP:Country")

PERMANOVA, INTERACTION GROUP:Country

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy : Country	1	318.719	2.077	0.005	0.325	0.488
pre_ltx vs post_ltx : Country	1	322.405	2.082	0.003	0.524	0.524
post_ltx vs healthy : Country	1	398.820	2.538	0.005	0.112	0.336

Interaction check

interaction_sig <- pp_fac.cov$pairs[pp_fac.cov$p.adjusted < 0.05]

if (length(interaction_sig)>0){
 for (i in 1:length(interaction_sig)){
  group1 <- unlist(strsplit(interaction_sig[i],split = " vs "))[1]
  group2 <- unlist(strsplit(interaction_sig[i],split = " vs "))[2]
  group2 <- unlist(strsplit(group2,split = " : "))[1]
  
  result_list <- adonis_postanalysis(x=pairwise_df,
                      factors = filt_colon_genus_metadata$Group,
                      covariate = filt_colon_genus_metadata$Country, 
                      group1 = group1,
                      group2 = group2,
                      patients = filt_colon_genus_metadata$Patient)
  print(result_list)
} 
}

Plots

PCoA custom

p <- pca_plot_custom(filt_colon_genus_tab,
                                 filt_colon_genus_taxa,
                                 filt_colon_genus_metadata,
                                 show_boxplots = TRUE,
                                 variable = "Group", size=2, 
                                 show_legend=TRUE)

# save the results
pca_plots_list[[paste(segment,level,"custom")]] <- p

# see the results
p

Saving results

write.xlsx(pairwise_aitchison_raw[[paste(level, segment)]], 
           file = file.path(path,
           paste0("beta_diversity_results_", segment,".xlsx")))

Supplementary analysis

Genus level

level="genus"

Bray-Curtis

PERMANOVA

pairwise_df <- filt_colon_genus_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,
                           filt_colon_genus_metadata$Group,
                           covariate = filt_colon_genus_metadata$Country, 
                           patients = filt_colon_genus_metadata$Patient,
                           sim.method = "bray", p.adjust.m="BH")

# interaction
pp_int <- pairwise.adonis(pairwise_df,
                          filt_colon_genus_metadata$Group,
                          covariate = filt_colon_genus_metadata$Country, 
                          patients = filt_colon_genus_metadata$Patient,
                          interaction = TRUE, sim.method = "bray", p.adjust.m="BH")

# tidy the results
pp_factor <- pp_main[[1]]
pp_cov <- pp_main[[2]]
pp_fac.cov <- pp_int[[3]]

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols; colnames(pp_cov) <- cols; colnames(pp_fac.cov) <- cols; 

# save raw results
supplements_beta[[paste("bray",level,segment)]] <- rbind(pp_factor,pp_cov,pp_fac.cov)

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	2.119	10.732	0.024	0.001	0.001	***
pre_ltx vs post_ltx	1	1.307	6.239	0.010	0.001	0.001	***
post_ltx vs healthy	1	3.286	17.150	0.031	0.001	0.001	***

knitr::kable(pp_cov,digits = 3,caption = "PERMANOVA, COUNTRY separation")

PERMANOVA, COUNTRY separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy , Country	1	2.862	14.497	0.033	0.001	0.001	***
pre_ltx vs post_ltx , Country	1	6.618	31.603	0.049	0.001	0.001	***
post_ltx vs healthy , Country	1	5.496	28.682	0.052	0.001	0.001	***

knitr::kable(pp_fac.cov,digits = 3,caption = "PERMANOVA, INTERACTION GROUP:Country")

PERMANOVA, INTERACTION GROUP:Country

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy : Country	1	0.783	3.993	0.009	0.012	0.018	*
pre_ltx vs post_ltx : Country	1	0.562	2.691	0.004	0.112	0.112
post_ltx vs healthy : Country	1	0.998	5.252	0.009	0.002	0.006	**

Interaction check

interaction_sig <- pp_fac.cov$pairs[pp_fac.cov$p.adjusted < 0.05]

for (i in 1:length(interaction_sig)){
  group1 <- unlist(strsplit(interaction_sig[i],split = " vs "))[1]
  group2 <- unlist(strsplit(interaction_sig[i],split = " vs "))[2]
  group2 <- unlist(strsplit(group2,split = " : "))[1]
  
  result_list <- adonis_postanalysis(x=pairwise_df,
                      factors = filt_colon_genus_metadata$Group,
                      covariate = filt_colon_genus_metadata$Country, 
                      patients = filt_colon_genus_metadata$Patient,
                      group1 = group1,
                      group2 = group2,
                      sim.method = 'bray')
  print(result_list)
}

## $pre_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs     R2      F Pr(>F) p.adjusted
## Fac        1    0.680 0.0271 3.7602   0.05       0.05
## Residual 135   24.413 0.9729                         
## Total    136   25.093 1.0000                         
## 
## $pre_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    2.222 0.03858 10.915  0.001  0.0013333
## Residual 272   55.360 0.96142                         
## Total    273   57.582 1.00000                         
## 
## $pre_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs     R2      F Pr(>F) p.adjusted
## Cov        1    2.166 0.0386 9.9564  0.001  0.0013333
## Residual 248   53.948 0.9614                         
## Total    249   56.114 1.0000                         
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1   1.4791 0.05417 9.1064  0.001  0.0013333
## Residual 159  25.8258 0.94583                         
## Total    160  27.3049 1.00000                         
## 
## $post_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    1.302 0.02297 7.0989  0.001      0.001
## Residual 302   55.371 0.97703                         
## Total    303   56.673 1.00000                         
## 
## $post_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    2.982 0.06786 14.924  0.001      0.001
## Residual 205   40.966 0.93214                         
## Total    206   43.949 1.00000                         
## 
## $post_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1    5.014 0.06639 24.748  0.001      0.001
## Residual 348   70.512 0.93361                         
## Total    349   75.526 1.00000                         
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1   1.4791 0.05417 9.1064  0.001      0.001
## Residual 159  25.8258 0.94583                         
## Total    160  27.3049 1.00000

Plots

p <- pca_plot_custom(filt_colon_genus_tab,
                                 filt_colon_genus_taxa,
                                 filt_colon_genus_metadata,
                                 measure = "bray",
                                 show_boxplots = TRUE,
                                 variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA bray",level,segment)]] <- p

# see the results
p

Jaccard

PERMANOVA

pairwise_df <- filt_colon_genus_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,
                           filt_colon_genus_metadata$Group,
                           covariate = filt_colon_genus_metadata$Country,
                           patients = filt_colon_genus_metadata$Patient,
                           sim.method = "jaccard", p.adjust.m="BH")

# interaction
pp_int <- pairwise.adonis(pairwise_df,
                          filt_colon_genus_metadata$Group,
                          covariate = filt_colon_genus_metadata$Country,
                          patients = filt_colon_genus_metadata$Patient,
                          interaction = TRUE, sim.method = "jaccard", p.adjust.m="BH")

# tidy the results
pp_factor <- pp_main[[1]]
pp_cov <- pp_main[[2]]
pp_fac.cov <- pp_int[[3]]

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols; colnames(pp_cov) <- cols; colnames(pp_fac.cov) <- cols; 

# save raw results
supplements_beta[[paste("jaccard",level,segment)]] <- rbind(pp_factor, 
                                                            pp_cov, 
                                                            pp_fac.cov)

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	2.225	7.844	0.018	0.001	0.002	**
pre_ltx vs post_ltx	1	1.382	4.680	0.007	0.002	0.002	**
post_ltx vs healthy	1	3.371	12.044	0.022	0.001	0.002	**

knitr::kable(pp_cov,digits = 3,caption = "PERMANOVA, COUNTRY separation")

PERMANOVA, COUNTRY separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy , Country	1	3.013	10.622	0.025	0.001	0.001	***
pre_ltx vs post_ltx , Country	1	6.474	21.918	0.035	0.001	0.001	***
post_ltx vs healthy , Country	1	5.595	19.994	0.037	0.001	0.001	***

knitr::kable(pp_fac.cov,digits = 3,caption = "PERMANOVA, INTERACTION GROUP:Country")

PERMANOVA, INTERACTION GROUP:Country

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy : Country	1	0.939	3.330	0.008	0.010	0.015	*
pre_ltx vs post_ltx : Country	1	0.696	2.362	0.004	0.163	0.163
post_ltx vs healthy : Country	1	1.134	4.078	0.008	0.001	0.003	**

Interaction check

interaction_sig <- pp_fac.cov$pairs[pp_fac.cov$p.adjusted < 0.05]

for (i in 1:length(interaction_sig)){
  group1 <- unlist(strsplit(interaction_sig[i],split = " vs "))[1]
  group2 <- unlist(strsplit(interaction_sig[i],split = " vs "))[2]
  group2 <- unlist(strsplit(group2,split = " : "))[1]
  
  result_list <- adonis_postanalysis(x=pairwise_df,
                      factors = filt_colon_genus_metadata$Group,
                      covariate = filt_colon_genus_metadata$Country, 
                      patients = filt_colon_genus_metadata$Patient,
                      group1 = group1,
                      group2 = group2,
                      sim.method = 'jaccard')
  print(result_list)
}

## $pre_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    0.759 0.02074 2.8587  0.061      0.061
## Residual 135   35.842 0.97926                         
## Total    136   36.601 1.00000                         
## 
## $pre_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    2.405 0.02957 8.2869  0.001  0.0013333
## Residual 272   78.937 0.97043                         
## Total    273   81.342 1.00000                         
## 
## $pre_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1    2.196 0.02856 7.2916  0.001  0.0013333
## Residual 248   74.696 0.97144                         
## Total    249   76.892 1.00000                         
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1    1.756 0.04196 6.9641  0.001  0.0013333
## Residual 159   40.083 0.95804                         
## Total    160   41.839 1.00000                         
## 
## $post_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    1.456 0.01743 5.3572  0.001      0.001
## Residual 302   82.098 0.98257                         
## Total    303   83.554 1.00000                         
## 
## $post_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    3.049 0.04918 10.604  0.001      0.001
## Residual 205   58.937 0.95082                         
## Total    206   61.986 1.00000                         
## 
## $post_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1    4.974 0.04696 17.147  0.001      0.001
## Residual 348  100.952 0.95304                         
## Total    349  105.926 1.00000                         
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1    1.756 0.04196 6.9641  0.001      0.001
## Residual 159   40.083 0.95804                         
## Total    160   41.839 1.00000

Plots

Custom

p <- pca_plot_custom(filt_colon_genus_tab,
                                 filt_colon_genus_taxa,
                                 filt_colon_genus_metadata,
                                 measure = "jaccard",
                                 show_boxplots = TRUE,
                                 variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA jaccard",level,segment)]] <- p

# see the results
p

ASV level

level="ASV"

Aitchison

# preparing data frame
pairwise_df <- filt_colon_asv_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(x=pairwise_df,
                          filt_colon_metadata$Group,
                           covariate = filt_colon_metadata$Country, 
                           sim.method = "robust.aitchison", 
                           p.adjust.m="BH",
                           patients = filt_colon_metadata$Patient)

# interaction
pp_int <- pairwise.adonis(pairwise_df,filt_colon_metadata$Group,
                          covariate = filt_colon_metadata$Country, 
                          interaction = TRUE, 
                          sim.method = "robust.aitchison", 
                          p.adjust.m="BH",
                          patients = filt_colon_metadata$Patient)

pp_factor <- pp_main[[1]]
pp_cov <- pp_main[[2]]
pp_fac.cov <- pp_int[[3]]

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols; colnames(pp_cov) <- cols; colnames(pp_fac.cov) <- cols; 

# save raw results
supplements_beta[[paste("aitchison",level,segment)]] <- rbind(pp_factor, 
                                                            pp_cov, 
                                                            pp_fac.cov)

# see the results
pp_factor

pp_cov

pp_fac.cov

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	1711.535	7.089	0.017	0.001	0.002	**
pre_ltx vs post_ltx	1	799.824	3.516	0.006	0.023	0.023	*
post_ltx vs healthy	1	2419.630	10.211	0.019	0.001	0.002	**

knitr::kable(pp_cov,digits = 3,caption = "PERMANOVA, COUNTRY separation")

PERMANOVA, COUNTRY separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy , Country	1	948.424	3.928	0.009	0.001	0.001	***
pre_ltx vs post_ltx , Country	1	1595.167	7.012	0.012	0.001	0.001	***
post_ltx vs healthy , Country	1	1870.942	7.895	0.015	0.001	0.001	***

knitr::kable(pp_fac.cov,digits = 3,caption = "PERMANOVA, INTERACTION GROUP:Country")

PERMANOVA, INTERACTION GROUP:Country

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy : Country	1	571.997	2.377	0.006	0.086	0.129
pre_ltx vs post_ltx : Country	1	439.338	1.934	0.003	0.768	0.768
post_ltx vs healthy : Country	1	626.621	2.653	0.005	0.023	0.069

Interaction check

interaction_sig <- pp_fac.cov$pairs[pp_fac.cov$p.adjusted < 0.05]

if (length(interaction_sig)>0){
 for (i in 1:length(interaction_sig)){
  group1 <- unlist(strsplit(interaction_sig[i],split = " vs "))[1]
  group2 <- unlist(strsplit(interaction_sig[i],split = " vs "))[2]
  group2 <- unlist(strsplit(group2,split = " : "))[1]
  
  result_list <- adonis_postanalysis(x=pairwise_df,
                      factors = filt_colon_metadata$Group,
                      covariate = filt_colon_metadata$Country, 
                      group1 = group1,
                      group2 = group2,
                      patients = filt_colon_metadata$Patient)
  print(result_list)
} 
}

PCoA

p <- pca_plot_custom(filt_colon_asv_tab,
                           filt_colon_taxa_tab,
                           filt_colon_metadata,
                           show_boxplots = TRUE,
                           variable = "Group", 
                           size=3, 
                           show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA aitchison",level,segment)]] <- p

# see the results
p

Bray-Curtis

PERMANOVA

# preparing data frame
pairwise_df <- filt_colon_asv_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,
                           filt_colon_metadata$Group,
                           covariate = filt_colon_metadata$Country,
                           patients = filt_colon_metadata$Patient,
                           sim.method = "bray", p.adjust.m="BH")

# interaction
pp_int <- pairwise.adonis(pairwise_df,
                          filt_colon_metadata$Group,
                          covariate = filt_colon_metadata$Country, 
                          patients = filt_colon_metadata$Patient,
                          interaction = TRUE, sim.method = "bray", p.adjust.m="BH")

pp_factor <- pp_main[[1]]
pp_cov <- pp_main[[2]]
pp_fac.cov <- pp_int[[3]]

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols; colnames(pp_cov) <- cols; colnames(pp_fac.cov) <- cols; 

# save raw results
supplements_beta[[paste("bray",level,segment)]] <- rbind(pp_factor, 
                                                            pp_cov, 
                                                            pp_fac.cov)

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	3.090	9.636	0.023	0.001	0.001	***
pre_ltx vs post_ltx	1	1.948	5.972	0.010	0.001	0.001	***
post_ltx vs healthy	1	4.788	15.423	0.029	0.001	0.001	***

knitr::kable(pp_cov,digits = 3,caption = "PERMANOVA, COUNTRY separation")

PERMANOVA, COUNTRY separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy , Country	1	2.383	7.429	0.017	0.001	0.001	***
pre_ltx vs post_ltx , Country	1	5.328	16.338	0.026	0.001	0.001	***
post_ltx vs healthy , Country	1	4.912	15.825	0.029	0.001	0.001	***

knitr::kable(pp_fac.cov,digits = 3,caption = "PERMANOVA, INTERACTION GROUP:Country")

PERMANOVA, INTERACTION GROUP:Country

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy : Country	1	0.901	2.821	0.007	0.040	0.060
pre_ltx vs post_ltx : Country	1	0.830	2.551	0.004	0.131	0.131
post_ltx vs healthy : Country	1	1.362	4.417	0.008	0.003	0.009	**

Interaction check

interaction_sig <- pp_fac.cov$pairs[pp_fac.cov$p.adjusted < 0.05]

for (i in 1:length(interaction_sig)){
  group1 <- unlist(strsplit(interaction_sig[i],split = " vs "))[1]
  group2 <- unlist(strsplit(interaction_sig[i],split = " vs "))[2]
  group2 <- unlist(strsplit(group2,split = " : "))[1]
  
  result_list <- adonis_postanalysis(x=pairwise_df,
                      factors = filt_colon_metadata$Group,
                      covariate = filt_colon_metadata$Country, 
                      group1 = group1,
                      group2 = group2,
                      patients = filt_colon_metadata$Patient,
                      sim.method = 'bray')
  print(result_list)
}

## $post_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    2.098 0.02219 6.8301  0.001      0.001
## Residual 301   92.461 0.97781                         
## Total    302   94.559 1.00000                         
## 
## $post_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    4.052 0.05993 13.068  0.001      0.001
## Residual 205   63.559 0.94007                         
## Total    206   67.611 1.00000                         
## 
## $post_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1    4.574 0.03999 14.456  0.001      0.001
## Residual 347  109.796 0.96001                         
## Total    348  114.370 1.00000                         
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1    1.700 0.03547 5.8478  0.001      0.001
## Residual 159   46.225 0.96453                         
## Total    160   47.925 1.00000

PCoA

p <- pca_plot_custom(filt_colon_asv_tab,
                     filt_colon_taxa_tab,
                     filt_colon_metadata,
                     measure = "bray",
                     show_boxplots = TRUE,
                     variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA bray",level,segment)]] <- p

# see the results
p

Jaccard

PERMANOVA

# preparing data frame
pairwise_df <- filt_colon_asv_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,
                           filt_colon_metadata$Group,
                           covariate = filt_colon_metadata$Country,
                           patients = filt_colon_metadata$Patient,
                           sim.method = "jaccard", p.adjust.m="BH")

# interaction
pp_int <- pairwise.adonis(pairwise_df,
                          filt_colon_metadata$Group,
                          covariate = filt_colon_metadata$Country, 
                          patients = filt_colon_metadata$Patient,
                          interaction = TRUE, sim.method = "jaccard", p.adjust.m="BH")

pp_factor <- pp_main[[1]]
pp_cov <- pp_main[[2]]
pp_fac.cov <- pp_int[[3]]

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols; colnames(pp_cov) <- cols; colnames(pp_fac.cov) <- cols; 

# save raw results
supplements_beta[[paste("jaccard",level,segment)]] <- rbind(pp_factor, 
                                                            pp_cov, 
                                                            pp_fac.cov)

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	2.453	6.302	0.015	0.001	0.001	***
pre_ltx vs post_ltx	1	1.695	4.312	0.007	0.001	0.001	***
post_ltx vs healthy	1	3.721	9.734	0.018	0.001	0.001	***

knitr::kable(pp_cov,digits = 3,caption = "PERMANOVA, COUNTRY separation")

PERMANOVA, COUNTRY separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy , Country	1	2.005	5.151	0.012	0.001	0.001	***
pre_ltx vs post_ltx , Country	1	4.185	10.649	0.017	0.001	0.001	***
post_ltx vs healthy , Country	1	3.933	10.288	0.020	0.001	0.001	***

knitr::kable(pp_fac.cov,digits = 3,caption = "PERMANOVA, INTERACTION GROUP:Country")

PERMANOVA, INTERACTION GROUP:Country

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy : Country	1	0.906	2.336	0.006	0.086	0.129
pre_ltx vs post_ltx : Country	1	0.862	2.199	0.004	0.291	0.291
post_ltx vs healthy : Country	1	1.291	3.393	0.006	0.004	0.012	*

Interaction check

interaction_sig <- pp_fac.cov$pairs[pp_fac.cov$p.adjusted < 0.05]

for (i in 1:length(interaction_sig)){
  group1 <- unlist(strsplit(interaction_sig[i],split = " vs "))[1]
  group2 <- unlist(strsplit(interaction_sig[i],split = " vs "))[2]
  group2 <- unlist(strsplit(group2,split = " : "))[1]
  
  result_list <- adonis_postanalysis(x=pairwise_df,
                      factors = filt_colon_metadata$Group,
                      covariate = filt_colon_metadata$Country, 
                      patients = filt_colon_metadata$Patient,
                      group1 = group1,
                      group2 = group2,
                      sim.method = 'jaccard')
  print(result_list)
}

## $post_ltx_healthy_CZ
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    1.758 0.01512 4.6216  0.001      0.001
## Residual 301  114.465 0.98488                         
## Total    302  116.223 1.00000                         
## 
## $post_ltx_healthy_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Fac, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Fac        1    3.254 0.04003 8.5476  0.001      0.001
## Residual 205   78.052 0.95997                         
## Total    206   81.306 1.00000                         
## 
## $post_ltx_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs      R2      F Pr(>F) p.adjusted
## Cov        1     3.68 0.02673 9.5297  0.001      0.001
## Residual 347   134.00 0.97327                         
## Total    348   137.68 1.00000                         
## 
## $healthy_CZ_vs_NO
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: supplied matrix
## Number of permutations: 999
## 
## adonis2(formula = x1 ~ Cov, data = x2, permutations = perm)
##           Df SumOfSqs     R2      F Pr(>F) p.adjusted
## Cov        1    1.544 0.0257 4.1941  0.001      0.001
## Residual 159   58.516 0.9743                         
## Total    160   60.059 1.0000

PCoA

p <- pca_plot_custom(filt_colon_asv_tab,
                     filt_colon_taxa_tab,
                     filt_colon_metadata,
                     measure = "jaccard",
                     show_boxplots = TRUE,
                     variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA jaccard",level,segment)]] <- p

# see the results
p

Saving results

write.xlsx(supplements_beta[!grepl("PCoA",names(supplements_beta))],
           file = file.path(path,
           paste0("supplements_beta_diversity_", segment,".xlsx")))

Univariate Analysis

Main - Genus level

level="genus"

# needed paths
path = "../results/Q1/univariate_analysis"
path_maaslin=file.path("../intermediate_files/maaslin/Q1",level)

# variables
raw_linda_results_genus[[segment]] <- list()
linda_results_genus[[segment]] <- list()

# country and interaction problems
list_country_union <- list()
list_intersections <- list()
list_venns <- list()
uni_statistics <- list()

# workbook for final df
wb <- createWorkbook()

# PSC effect
psc_effect <- list()

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

colon_genus_asv_taxa_tab <- create_asv_taxa_table(colon_genus_tab,
                                                  colon_genus_taxa_tab)

pre_ltx vs healthy

linDA

group <- c("healthy","pre_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

# prepare the data
linda_data <- binomial_prep(colon_genus_tab,
                            colon_genus_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

## Removing 135 ASV(s)

## Removing 10 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data,
                   filt_colon_uni_metadata,
                   formula = '~ Group * Country + (1|Patient)')

## 0  features are filtered!
## The filtered data has  411  samples and  148  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")


for (grp in c(group1,group2,group3)){
  raw_linda_results_genus[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results_genus[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1, 
                                taxa_table = filt_colon_uni_taxa) + 
            ggtitle(paste(group,collapse=" vs "))

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                taxa_table = filt_colon_uni_taxa) + 
            ggtitle("Country effect") 

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_colon_uni_taxa) +
            ggtitle("Interaction")

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

# see the plot
volcano

## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider increasing max.overlaps
## ggrepel: 1 unlabeled data points (too many overlaps). Consider increasing max.overlaps

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano2 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa,variable="Country") + 
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)

## Warning: Removed 30 rows containing missing values or values outside the scale range (`geom_text_repel()`).

## Warning: Removed 14 rows containing missing values or values outside the scale range (`geom_text_repel()`).

volcano

## Warning: ggrepel: 20 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_genus, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_colon_uni_data,
                                          filt_colon_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_genus[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "pre_ltx"
## [1] "healthy"
## [1] "pre_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- comparison_name
addWorksheet(wb, sheetName = new_name)
writeData(wb, sheet = new_name, uni_df, rowNames=FALSE)

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_genus_tab,
                            colon_genus_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

## Removing 17 ASV(s)

## Removing 10 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data,
                   filt_colon_uni_metadata,
                   formula = '~ Group * Country + (1|Patient)')

## 0  features are filtered!
## The filtered data has  600  samples and  130  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results_genus[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results_genus[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1, 
                                taxa_table = filt_colon_uni_taxa) + 
            ggtitle(paste(group,collapse=" vs "))

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                taxa_table = filt_colon_uni_taxa) + 
            ggtitle("Country effect") 

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_colon_uni_taxa) +
            ggtitle("Interaction")

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

# see the plot
volcano

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano2 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa,variable="Country") + 
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)

## Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text_repel()`).

## Warning: Removed 7 rows containing missing values or values outside the scale range (`geom_text_repel()`).

volcano

Group - Intersection

intersection_results <- group_intersection(group, 
                                           list_intersections, 
                                           list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_genus, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_colon_uni_data,
                                          filt_colon_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_genus[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "pre_ltx"
## [1] "post_ltx"
## [1] "pre_ltx"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- comparison_name
addWorksheet(wb, sheetName = new_name)
writeData(wb, sheet = new_name, uni_df, rowNames=FALSE)

post_ltx vs healthy

group <- c("healthy","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_genus_tab,
                            colon_genus_taxa_tab,
                            colon_metadata,group,
                            usage="linDA")

## Removing 71 ASV(s)

## Removing 5 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data,
                   filt_colon_uni_metadata,
                   formula = '~ Group * Country + (1|Patient)')

## 0  features are filtered!
## The filtered data has  511  samples and  153  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results_genus[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results_genus[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1, 
                                taxa_table = filt_colon_uni_taxa) + 
            ggtitle(paste(group,collapse=" vs "))

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                taxa_table = filt_colon_uni_taxa) + 
            ggtitle("Country effect") 

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_colon_uni_taxa) +
            ggtitle("Interaction")

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

# see the plot
volcano

## Warning: ggrepel: 36 unlabeled data points (too many overlaps). Consider increasing max.overlaps

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano2 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa,variable="Country") + 
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)

## Warning: Removed 32 rows containing missing values or values outside the scale range (`geom_text_repel()`).

## Warning: Removed 18 rows containing missing values or values outside the scale range (`geom_text_repel()`).

volcano

## Warning: ggrepel: 29 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_genus, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_colon_uni_data,
                                          filt_colon_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_genus[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "post_ltx"
## [1] "healthy"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- comparison_name
addWorksheet(wb, sheetName = new_name)
writeData(wb, sheet = new_name, uni_df, rowNames=FALSE)

Visualization

Heatmap visualizing the linDA’s logFoldChange for taxa with p < 0.1.

list_heatmap <- list_intersections[grep(paste(segment,level),
                                  names(list_intersections),value=TRUE)]

p_heatmap_linda <- heatmap_linda(list_heatmap,colon_taxa_tab)
p_heatmap_linda

Dot heatmap

dotheatmap_linda <- dot_heatmap_linda(list_heatmap,
                                      uni_df,
                                      colon_taxa_tab)

## min_clr -2.040697 
## max_clr 6.85029 
## min_log -3.771476 
## max_log 5.049706

dotheatmap_linda

PSC effect

pre_LTx vs Healthy and Post_LTx vs Healthy intersection

imagename

A <- list_intersections[[paste(segment,level,"healthy vs pre_ltx")]]$SeqID
B <- list_intersections[[paste(segment,level,"healthy vs post_ltx")]]$SeqID
C <- intersect(A,B)

psc_effect[[paste(segment,level)]] <- list_intersections[[paste(segment,level, "healthy vs pre_ltx")]][list_intersections[[paste(segment,level, "healthy vs pre_ltx")]]$SeqID %in% C,]
  
# see the results
psc_effect[[paste(segment,level)]] 

Saving results

# ALL DATA
saveWorkbook(wb,file.path(path,paste0("uni_analysis_wb_",segment,".xlsx")),
             overwrite = TRUE)

# PSC effect
write.xlsx(psc_effect[[paste(segment,level)]],file.path(path,paste0("psc_effect_",segment,".xlsx")))

# SIGNIFICANT taxa

write.xlsx(list_intersections[grepl(segment,names(list_intersections))] %>%
            `names<-`(gsub(segment, "", names(
              list_intersections[grepl(segment,names(list_intersections))]))),
           file.path(path,paste0("significant_taxa_",segment,".xlsx")))

Supplementary Analysis

ASV level

level="ASV"

path_maaslin="../intermediate_files/maaslin/Q1/ASV/"

raw_linda_results[[segment]] <- list()
linda_results[[segment]] <- list()
supplements_wb <- createWorkbook()

pre_ltx vs healthy

group <- c("healthy","pre_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_asv_tab,
                            colon_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

## Removing 2138 ASV(s)

## Removing 70 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data, 
                   filt_colon_uni_metadata, 
                   formula = '~ Group * Country + (1|Patient)')

## 0  features are filtered!
## The filtered data has  411  samples and  368  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1,
                                taxa_table = filt_colon_uni_taxa) +
              ggtitle(paste(group,collapse=" vs "))

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                 taxa_table = filt_colon_uni_taxa) + 
              ggtitle("Country effect")

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_colon_uni_taxa) + 
              ggtitle("Interaction")

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

# see the plot
volcano

## Warning: ggrepel: 18 unlabeled data points (too many overlaps). Consider increasing max.overlaps

## Warning: ggrepel: 27 unlabeled data points (too many overlaps). Consider increasing max.overlaps

MaAsLin2

Volcano plot

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano2 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa,variable="Country") +
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)

# see the results
volcano

## Warning: ggrepel: 141 unlabeled data points (too many overlaps). Consider increasing max.overlaps

## Warning: ggrepel: 16 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                                                                      segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                                    filt_colon_uni_data,
                                                    filt_colon_uni_metadata,
                                                    segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "pre_ltx"
## [1] "healthy"
## [1] "pre_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_asv_tab,colon_taxa_tab,
                            colon_metadata,group, usage="linDA")

## Removing 1157 ASV(s)

## Removing 52 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data, 
                   filt_colon_uni_metadata, 
                   formula = '~ Group * Country + (1|Patient)')

## 0  features are filtered!
## The filtered data has  599  samples and  249  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1,
                                taxa_table = filt_colon_uni_taxa) +
              ggtitle(paste(group,collapse=" vs "))

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                 taxa_table = filt_colon_uni_taxa) + 
              ggtitle("Country effect")

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_colon_uni_taxa) + 
              ggtitle("Interaction")

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

# see the plot
volcano

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano2 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa,variable="Country") +
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)

# see the results
volcano

## Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                           segment=segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_colon_uni_data,
                                          filt_colon_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                               segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "pre_ltx"
## [1] "post_ltx"
## [1] "pre_ltx"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

post_ltx vs healthy

group <- c("healthy","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_asv_tab,
                            colon_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

## Removing 1096 ASV(s)

## Removing 50 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data, 
                   filt_colon_uni_metadata,
                   formula = '~ Group * Country + (1|Patient)')

## 0  features are filtered!
## The filtered data has  510  samples and  370  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1,
                                taxa_table = filt_colon_uni_taxa) +
              ggtitle(paste(group,collapse=" vs "))

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                 taxa_table = filt_colon_uni_taxa) + 
              ggtitle("Country effect")

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_colon_uni_taxa) + 
              ggtitle("Interaction")

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

# see the plot
volcano

## Warning: ggrepel: 72 unlabeled data points (too many overlaps). Consider increasing max.overlaps

## Warning: ggrepel: 18 unlabeled data points (too many overlaps). Consider increasing max.overlaps

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano2 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa,variable="Country") + 
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)
volcano

## Warning: ggrepel: 154 unlabeled data points (too many overlaps). Consider increasing max.overlaps

## Warning: ggrepel: 60 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_colon_uni_data,
                                          filt_colon_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "post_ltx"
## [1] "healthy"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

Visualization

Heatmap visualizing the linDA’s logFoldChange for taxa with p < 0.1.

list_heatmap <- list_intersections[grep(paste(segment,level),
                                  names(list_intersections),value=TRUE)]

p_heatmap_linda <- heatmap_linda(list_heatmap,colon_taxa_tab)
p_heatmap_linda

Dot heatmap

dotheatmap_linda <- dot_heatmap_linda(list_heatmap,uni_df,colon_taxa_tab)

## min_clr -1.17843 
## max_clr 5.07243 
## min_log -4.431053 
## max_log 4.150355

dotheatmap_linda

PSC effect

pre_LTx vs Healthy and Post_LTx vs Healthy intersection

imagename

A <- list_intersections[[paste(segment,level,"healthy vs pre_ltx")]]$SeqID
B <- list_intersections[[paste(segment,level,"healthy vs post_ltx")]]$SeqID
C <- intersect(A,B)

psc_effect[[paste(segment,level)]] <- list_intersections[[paste(segment,level, "healthy vs pre_ltx")]][list_intersections[[paste(segment,level, "healthy vs pre_ltx")]]$SeqID %in% C,]
  
# see the results
psc_effect[[paste(segment,level)]] 

Phylum level

level="phylum"

path_maaslin="../intermediate_files/maaslin/Q1/Phylum/"

raw_linda_results_phylum[[segment]] <- list()
linda_results_phylum[[segment]] <- list()

Aggregate taxa

phylum_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = "Phylum")

colon_phylum_tab <- phylum_data[[1]]
colon_phylum_taxa_tab <- phylum_data[[2]]

pre_ltx vs healthy

group <- c("healthy","pre_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_phylum_tab,
                            colon_phylum_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

## Removing 2 ASV(s)

## Removing 1 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data, 
                   filt_colon_uni_metadata, 
                   formula = '~ Group * Country + (1|Patient)')

## 0  features are filtered!
## The filtered data has  411  samples and  8  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1,
                                taxa_table = filt_colon_uni_taxa) +
              ggtitle(paste(group,collapse=" vs "))

## Using Phylum for naming

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                 taxa_table = filt_colon_uni_taxa) + 
              ggtitle("Country effect")

## Using Phylum for naming

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_colon_uni_taxa) + 
              ggtitle("Interaction")

## Using Phylum for naming

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

# see the plot
volcano

MaAsLin2

Volcano plot

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano2 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa,variable="Country") +
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)

# see the results
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                                                                      segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                                    filt_colon_uni_data,
                                                    filt_colon_uni_metadata,
                                                    segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "pre_ltx"
## [1] "healthy"
## [1] "pre_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_phylum_tab,
                            colon_phylum_taxa_tab,
                            colon_metadata,group, usage="linDA")

## Removing 1 ASV(s)
## Removing 1 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data, 
                   filt_colon_uni_metadata, 
                   formula = '~ Group * Country + (1|Patient)')

## 0  features are filtered!
## The filtered data has  600  samples and  8  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1,
                                taxa_table = filt_colon_uni_taxa) +
              ggtitle(paste(group,collapse=" vs "))

## Using Phylum for naming

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                 taxa_table = filt_colon_uni_taxa) + 
              ggtitle("Country effect")

## Using Phylum for naming

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_colon_uni_taxa) + 
              ggtitle("Interaction")

## Using Phylum for naming

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

# see the plot
volcano

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano2 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa,variable="Country") +
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)

# see the results
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                           segment=segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_colon_uni_data,
                                          filt_colon_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                               segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "pre_ltx"
## [1] "post_ltx"
## [1] "pre_ltx"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

post_ltx vs healthy

group <- c("healthy","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_phylum_tab,
                            colon_phylum_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

## Removing 2 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data, 
                   filt_colon_uni_metadata,
                   formula = '~ Group * Country + (1|Patient)')

## 0  features are filtered!
## The filtered data has  511  samples and  8  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])
group2 <- paste0(group[1], " , ",group[2], " - CZ vs NO") 
group3 <- paste0(group[1], " vs ","Group",group[2], ":CountryNO")

for (grp in c(group1,group2,group3)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1,
                                taxa_table = filt_colon_uni_taxa) +
              ggtitle(paste(group,collapse=" vs "))

## Using Phylum for naming

volcano_2  <- volcano_plot_linda(linda.output, group2, 
                                 taxa_table = filt_colon_uni_taxa) + 
              ggtitle("Country effect")

## Using Phylum for naming

volcano_3 <- volcano_plot_linda(linda.output, group3, 
                                taxa_table = filt_colon_uni_taxa) + 
              ggtitle("Interaction")

## Using Phylum for naming

volcano <- ggarrange(volcano_1,volcano_2,volcano_3, ncol=3)

# see the plot
volcano

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano2 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa,variable="Country") + 
            ggtitle("Country effect")

volcano <- ggarrange(volcano1,volcano2, ncol=2)
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Country - Union

list_country_union <- country_union(group,linda.output, fit_data,
                                    segment=segment,
                                    level=level)

Interaction effect

list_interaction_significant <- country_interaction(group,
                                                    linda.output, 
                                                    list_intersections,
                                          filt_colon_uni_data,
                                          filt_colon_uni_metadata,
                                          segment=segment,
                                          level=level)

# see the result
## significant interaction effect
list_interaction_significant[[1]]

## NULL

## results for czech cohort
list_interaction_significant[[2]]

## [1] NA

## results for norwegian cohort
list_interaction_significant[[3]]

## [1] NA

Removing problematic taxa

list_intersections <- removing_interaction_problems(group,
                                                    list_interaction_significant,
                                                    list_intersections,
                                                    segment=segment,
                                                    level=level)

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "post_ltx"
## [1] "healthy"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

Visualization

Heatmap visualizing the linDA’s logFoldChange for taxa with p < 0.1.

list_heatmap <- list_intersections[grep(paste(segment,level),
                                  names(list_intersections),value=TRUE)]

p_heatmap_linda <- heatmap_linda(list_heatmap,colon_taxa_tab)
p_heatmap_linda

Dot heatmap

dotheatmap_linda <- dot_heatmap_linda(list_heatmap,uni_df,colon_taxa_tab)

## min_clr -4.297728 
## max_clr 4.833311 
## min_log -2.728687 
## max_log 2.86356

dotheatmap_linda

PSC effect

pre_LTx vs Healthy and Post_LTx vs Healthy intersection

imagename

A <- list_intersections[[paste(segment,level,"healthy vs pre_ltx")]]$SeqID
B <- list_intersections[[paste(segment,level,"healthy vs post_ltx")]]$SeqID
C <- intersect(A,B)

psc_effect[[paste(segment,level)]] <- list_intersections[[paste(segment,level, "healthy vs pre_ltx")]][list_intersections[[paste(segment,level, "healthy vs pre_ltx")]]$SeqID %in% C,]
  
# see the results
psc_effect[[paste(segment,level)]] 

Machine learning

path = "../results/Q1/models"

ElasticNet

model="enet"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 2138 ASV(s)

## Removing 70 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                        [,1]
## alpha                           0.600000000
## lambda                          0.006418715
## auc                             1.000000000
## auc_czech                               NaN
## auc_no                          1.000000000
## auc_optimism_corrected          0.899803944
## auc_optimism_corrected_CIL      0.862554452
## auc_optimism_corrected_CIU      0.954837040
## accuracy                        1.000000000
## accuracy_czech                          NaN
## accuracy_no                     1.000000000
## accuracy_optimism_corrected     0.854825924
## accuracy_optimism_corrected_CIL 0.763185352
## accuracy_optimism_corrected_CIU 0.909881423

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0  66   0
##    1   0 208
## 
## $czech
## [1] NaN
## 
## $no
##     Predicted
## True   0   1
##    0  66   0
##    1   0 208

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 1157 ASV(s)

## Removing 52 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                        [,1]
## alpha                           0.400000000
## lambda                          0.002321497
## auc                             1.000000000
## auc_czech                               NaN
## auc_no                          1.000000000
## auc_optimism_corrected          0.776893742
## auc_optimism_corrected_CIL      0.722118098
## auc_optimism_corrected_CIU      0.835243907
## accuracy                        1.000000000
## accuracy_czech                          NaN
## accuracy_no                     1.000000000
## accuracy_optimism_corrected     0.720985227
## accuracy_optimism_corrected_CIL 0.675597312
## accuracy_optimism_corrected_CIU 0.782035468

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0 141   0
##    1   0 208
## 
## $czech
## [1] NaN
## 
## $no
##     Predicted
## True   0   1
##    0 141   0
##    1   0 208

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 1096 ASV(s)

## Removing 50 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                        [,1]
## alpha                           0.400000000
## lambda                          0.009181833
## auc                             1.000000000
## auc_czech                               NaN
## auc_no                          1.000000000
## auc_optimism_corrected          0.912892293
## auc_optimism_corrected_CIL      0.726281375
## auc_optimism_corrected_CIU      0.996938776
## accuracy                        1.000000000
## accuracy_czech                          NaN
## accuracy_no                     1.000000000
## accuracy_optimism_corrected     0.865169592
## accuracy_optimism_corrected_CIL 0.665824049
## accuracy_optimism_corrected_CIU 0.985714286

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0  66   0
##    1   0 141
## 
## $czech
## [1] NaN
## 
## $no
##     Predicted
## True   0   1
##    0  66   0
##    1   0 141

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 135 ASV(s)

## Removing 10 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                        [,1]
## alpha                           1.000000000
## lambda                          0.002002496
## auc                             1.000000000
## auc_czech                               NaN
## auc_no                          1.000000000
## auc_optimism_corrected          0.939129742
## auc_optimism_corrected_CIL      0.873705846
## auc_optimism_corrected_CIU      0.972359287
## accuracy                        1.000000000
## accuracy_czech                          NaN
## accuracy_no                     1.000000000
## accuracy_optimism_corrected     0.897874319
## accuracy_optimism_corrected_CIL 0.822478098
## accuracy_optimism_corrected_CIU 0.941397825

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0  66   0
##    1   0 208
## 
## $czech
## [1] NaN
## 
## $no
##     Predicted
## True   0   1
##    0  66   0
##    1   0 208

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 17 ASV(s)

## Removing 10 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                        [,1]
## alpha                           0.400000000
## lambda                          0.004668847
## auc                             1.000000000
## auc_czech                               NaN
## auc_no                          1.000000000
## auc_optimism_corrected          0.719529792
## auc_optimism_corrected_CIL      0.610100571
## auc_optimism_corrected_CIU      0.811366490
## accuracy                        1.000000000
## accuracy_czech                          NaN
## accuracy_no                     1.000000000
## accuracy_optimism_corrected     0.672240876
## accuracy_optimism_corrected_CIL 0.559724912
## accuracy_optimism_corrected_CIU 0.777542460

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0 141   0
##    1   0 208
## 
## $czech
## [1] NaN
## 
## $no
##     Predicted
## True   0   1
##    0 141   0
##    1   0 208

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 71 ASV(s)

## Removing 5 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group",
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                         [,1]
## alpha                           0.2000000000
## lambda                          0.0005131181
## auc                             1.0000000000
## auc_czech                                NaN
## auc_no                          1.0000000000
## auc_optimism_corrected          0.9437453910
## auc_optimism_corrected_CIL      0.8534746657
## auc_optimism_corrected_CIU      0.9922908724
## accuracy                        1.0000000000
## accuracy_czech                           NaN
## accuracy_no                     1.0000000000
## accuracy_optimism_corrected     0.8783399977
## accuracy_optimism_corrected_CIL 0.7556653353
## accuracy_optimism_corrected_CIU 0.9366636528

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0  66   0
##    1   0 141
## 
## $czech
## [1] NaN
## 
## $no
##     Predicted
## True   0   1
##    0  66   0
##    1   0 141

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

Saving results

models_summ_df_colon <- do.call(rbind, 
  models_summ[grep(segment,names(models_summ),value = TRUE)])

write.csv(models_summ_df_colon,file.path(path,paste0("elastic_net_",segment,".csv")))

Supplementary models

CLR-transformed data

kNN

model="knn"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 2138 ASV(s)

## Removing 70 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.9869974
## auc_optimism_corrected           0.7759750
## auc_optimism_corrected_CIL       0.6782309
## auc_optimism_corrected_CIU       0.8456175
## accuracy                         0.8248175
## accuracy_optimism_corrected      0.7595124
## accuracy_optimism_corrected_CIL  0.7042451
## accuracy_optimism_corrected_CIU  0.8175148

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)


# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 1157 ASV(s)

## Removing 52 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.9257194
## auc_optimism_corrected           0.5786678
## auc_optimism_corrected_CIL       0.5341015
## auc_optimism_corrected_CIU       0.6370718
## accuracy                         0.8452722
## accuracy_optimism_corrected      0.5668339
## accuracy_optimism_corrected_CIL  0.5115916
## accuracy_optimism_corrected_CIU  0.6118076

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 1096 ASV(s)

## Removing 50 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.9760370
## auc_optimism_corrected           0.8605140
## auc_optimism_corrected_CIL       0.7570092
## auc_optimism_corrected_CIU       0.9766050
## accuracy                         0.8937198
## accuracy_optimism_corrected      0.7931394
## accuracy_optimism_corrected_CIL  0.6842176
## accuracy_optimism_corrected_CIU  0.8788913

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 135 ASV(s)

## Removing 10 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               11.0000000
## auc                              0.9403045
## auc_optimism_corrected           0.8474865
## auc_optimism_corrected_CIL       0.7613057
## auc_optimism_corrected_CIU       0.8879084
## accuracy                         0.8832117
## accuracy_optimism_corrected      0.8062435
## accuracy_optimism_corrected_CIL  0.7098733
## accuracy_optimism_corrected_CIU  0.8462698

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 17 ASV(s)

## Removing 10 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               18.0000000
## auc                              0.8269060
## auc_optimism_corrected           0.5211152
## auc_optimism_corrected_CIL       0.4241497
## auc_optimism_corrected_CIU       0.6071406
## accuracy                         0.7191977
## accuracy_optimism_corrected      0.5276003
## accuracy_optimism_corrected_CIL  0.4455386
## accuracy_optimism_corrected_CIU  0.5700858

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 71 ASV(s)

## Removing 5 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.9692671
## auc_optimism_corrected           0.8992102
## auc_optimism_corrected_CIL       0.8248738
## auc_optimism_corrected_CIU       0.9877840
## accuracy                         0.8937198
## accuracy_optimism_corrected      0.8169337
## accuracy_optimism_corrected_CIL  0.7354628
## accuracy_optimism_corrected_CIU  0.9507393

roc_c

Random Forest

model="rf"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 2138 ASV(s)

## Removing 70 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "117"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9497941"
## auc_optimism_corrected_CIL      "0.8885346"
## auc_optimism_corrected_CIU      "0.9896912"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.921224" 
## accuracy_optimism_corrected_CIL "0.8561052"
## accuracy_optimism_corrected_CIU "0.9715691"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 1157 ASV(s)

## Removing 52 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "99"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.6808152"
## auc_optimism_corrected_CIL      "0.5804338"
## auc_optimism_corrected_CIU      "0.7472019"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.634459" 
## accuracy_optimism_corrected_CIL "0.5855093"
## accuracy_optimism_corrected_CIU "0.7213764"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 1096 ASV(s)

## Removing 50 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "363"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.8699486"
## auc_optimism_corrected_CIL      "0.7779758"
## auc_optimism_corrected_CIU      "0.9516733"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.823617" 
## accuracy_optimism_corrected_CIL "0.708467" 
## accuracy_optimism_corrected_CIU "0.9283133"

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 135 ASV(s)

## Removing 10 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "37"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.8886183"
## auc_optimism_corrected_CIL      "0.7615653"
## auc_optimism_corrected_CIU      "0.9500028"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8510081"
## accuracy_optimism_corrected_CIL "0.748883" 
## accuracy_optimism_corrected_CIU "0.9115807"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 17 ASV(s)

## Removing 10 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "35"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.6793403"
## auc_optimism_corrected_CIL      "0.6124719"
## auc_optimism_corrected_CIU      "0.7595152"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.6269262"
## accuracy_optimism_corrected_CIL "0.5617618"
## accuracy_optimism_corrected_CIU "0.6826535"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 71 ASV(s)

## Removing 5 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "15"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.921861" 
## auc_optimism_corrected_CIL      "0.8673639"
## auc_optimism_corrected_CIU      "0.9623318"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8702487"
## accuracy_optimism_corrected_CIL "0.774093" 
## accuracy_optimism_corrected_CIU "0.9292183"

roc_c

Gradient boosting

model="gb"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 2138 ASV(s)

## Removing 70 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         200.0000000
## interaction.depth                 3.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   20.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9478179
## auc_optimism_corrected_CIL        0.8967442
## auc_optimism_corrected_CIU        0.9890405
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.9210035
## accuracy_optimism_corrected_CIL   0.8821244
## accuracy_optimism_corrected_CIU   0.9521559

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 1157 ASV(s)

## Removing 52 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs


# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 5.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   20.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.7421564
## auc_optimism_corrected_CIL        0.6987686
## auc_optimism_corrected_CIU        0.8168118
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.6828691
## accuracy_optimism_corrected_CIL   0.6296101
## accuracy_optimism_corrected_CIU   0.7173576

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 1096 ASV(s)

## Removing 50 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs


# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 3.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   20.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9364056
## auc_optimism_corrected_CIL        0.8819631
## auc_optimism_corrected_CIU        0.9989796
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.8943361
## accuracy_optimism_corrected_CIL   0.8246301
## accuracy_optimism_corrected_CIU   0.9805556

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 135 ASV(s)

## Removing 10 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 1.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   30.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9505719
## auc_optimism_corrected_CIL        0.8673730
## auc_optimism_corrected_CIU        0.9898829
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.9006813
## accuracy_optimism_corrected_CIL   0.7725822
## accuracy_optimism_corrected_CIU   0.9452636

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 17 ASV(s)

## Removing 10 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 5.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   20.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.7094297
## auc_optimism_corrected_CIL        0.6518347
## auc_optimism_corrected_CIU        0.7550652
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.6583659
## accuracy_optimism_corrected_CIL   0.5783533
## accuracy_optimism_corrected_CIU   0.7616620

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 71 ASV(s)

## Removing 5 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         200.0000000
## interaction.depth                 3.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   10.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9315326
## auc_optimism_corrected_CIL        0.8695649
## auc_optimism_corrected_CIU        0.9969388
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.9004446
## accuracy_optimism_corrected_CIL   0.7844430
## accuracy_optimism_corrected_CIU   0.9928571

roc_c

Relative abundances

Elastic net

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 2138 ASV(s)

## Removing 70 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                        [,1]
## alpha                           0.400000000
## lambda                          0.009606311
## auc                             1.000000000
## auc_czech                               NaN
## auc_no                          1.000000000
## auc_optimism_corrected          0.947591655
## auc_optimism_corrected_CIL      0.899070840
## auc_optimism_corrected_CIU      0.974258612
## accuracy                        1.000000000
## accuracy_czech                          NaN
## accuracy_no                     1.000000000
## accuracy_optimism_corrected     0.892267728
## accuracy_optimism_corrected_CIL 0.862237493
## accuracy_optimism_corrected_CIU 0.922722017

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 1157 ASV(s)

## Removing 52 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.20000000
## lambda                          0.01370467
## auc                             1.00000000
## auc_czech                              NaN
## auc_no                          1.00000000
## auc_optimism_corrected          0.62011993
## auc_optimism_corrected_CIL      0.46329550
## auc_optimism_corrected_CIU      0.70305244
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                     1.00000000
## accuracy_optimism_corrected     0.61154629
## accuracy_optimism_corrected_CIL 0.51705662
## accuracy_optimism_corrected_CIU 0.68614943

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)


# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 1096 ASV(s)

## Removing 50 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.20000000
## lambda                          0.01558488
## auc                             1.00000000
## auc_czech                              NaN
## auc_no                          1.00000000
## auc_optimism_corrected          0.93333079
## auc_optimism_corrected_CIL      0.83566515
## auc_optimism_corrected_CIU      0.99515723
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                     1.00000000
## accuracy_optimism_corrected     0.89119078
## accuracy_optimism_corrected_CIL 0.80403241
## accuracy_optimism_corrected_CIU 0.96838262

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 135 ASV(s)

## Removing 10 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                        [,1]
## alpha                           0.800000000
## lambda                          0.008018801
## auc                             1.000000000
## auc_czech                               NaN
## auc_no                          1.000000000
## auc_optimism_corrected          0.932350797
## auc_optimism_corrected_CIL      0.876282619
## auc_optimism_corrected_CIU      0.967590093
## accuracy                        1.000000000
## accuracy_czech                          NaN
## accuracy_no                     1.000000000
## accuracy_optimism_corrected     0.867933194
## accuracy_optimism_corrected_CIL 0.835989305
## accuracy_optimism_corrected_CIU 0.909881423

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)


# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 17 ASV(s)

## Removing 10 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.40000000
## lambda                          0.01329698
## auc                             0.99440807
## auc_czech                              NaN
## auc_no                          0.99440807
## auc_optimism_corrected          0.56903725
## auc_optimism_corrected_CIL      0.49166629
## auc_optimism_corrected_CIU      0.70965158
## accuracy                        0.95988539
## accuracy_czech                         NaN
## accuracy_no                     0.95988539
## accuracy_optimism_corrected     0.57568545
## accuracy_optimism_corrected_CIL 0.51044438
## accuracy_optimism_corrected_CIU 0.69125450

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 71 ASV(s)

## Removing 5 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.20000000
## lambda                          0.01423325
## auc                             1.00000000
## auc_czech                              NaN
## auc_no                          1.00000000
## auc_optimism_corrected          0.93130864
## auc_optimism_corrected_CIL      0.82913832
## auc_optimism_corrected_CIU      0.99967201
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                     1.00000000
## accuracy_optimism_corrected     0.88561761
## accuracy_optimism_corrected_CIL 0.77563966
## accuracy_optimism_corrected_CIU 0.96448141

roc_c

kNN

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 2138 ASV(s)

## Removing 70 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               13.0000000
## auc                              0.9579691
## auc_optimism_corrected           0.8543447
## auc_optimism_corrected_CIL       0.7234433
## auc_optimism_corrected_CIU       0.9347518
## accuracy                         0.9160584
## accuracy_optimism_corrected      0.8231328
## accuracy_optimism_corrected_CIL  0.7320056
## accuracy_optimism_corrected_CIU  0.9164786

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 1157 ASV(s)

## Removing 52 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               21.0000000
## auc                              0.7521652
## auc_optimism_corrected           0.5417615
## auc_optimism_corrected_CIL       0.4604685
## auc_optimism_corrected_CIU       0.6299550
## accuracy                         0.7134670
## accuracy_optimism_corrected      0.5406239
## accuracy_optimism_corrected_CIL  0.4521977
## accuracy_optimism_corrected_CIU  0.6040405

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 1096 ASV(s)

## Removing 50 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.9742639
## auc_optimism_corrected           0.8879882
## auc_optimism_corrected_CIL       0.8419757
## auc_optimism_corrected_CIU       0.9598700
## accuracy                         0.8985507
## accuracy_optimism_corrected      0.7933714
## accuracy_optimism_corrected_CIL  0.7270877
## accuracy_optimism_corrected_CIU  0.8956349

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 135 ASV(s)

## Removing 10 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               13.0000000
## auc                              0.9594988
## auc_optimism_corrected           0.8591223
## auc_optimism_corrected_CIL       0.7808096
## auc_optimism_corrected_CIU       0.9187468
## accuracy                         0.8978102
## accuracy_optimism_corrected      0.8000668
## accuracy_optimism_corrected_CIL  0.7431141
## accuracy_optimism_corrected_CIU  0.8514205

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 17 ASV(s)

## Removing 10 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.8530244
## auc_optimism_corrected           0.5667836
## auc_optimism_corrected_CIL       0.4945097
## auc_optimism_corrected_CIU       0.6440563
## accuracy                         0.7707736
## accuracy_optimism_corrected      0.5636036
## accuracy_optimism_corrected_CIL  0.4960163
## accuracy_optimism_corrected_CIU  0.6227429

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 71 ASV(s)

## Removing 5 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               11.0000000
## auc                              0.9673866
## auc_optimism_corrected           0.8764406
## auc_optimism_corrected_CIL       0.8109556
## auc_optimism_corrected_CIU       0.9573980
## accuracy                         0.8937198
## accuracy_optimism_corrected      0.7743467
## accuracy_optimism_corrected_CIL  0.6910492
## accuracy_optimism_corrected_CIU  0.8240199

roc_c

Random Forest

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 2138 ASV(s)

## Removing 70 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "31"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.979444" 
## auc_optimism_corrected_CIL      "0.9341725"
## auc_optimism_corrected_CIU      "0.9998634"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.9463362"
## accuracy_optimism_corrected_CIL "0.9113375"
## accuracy_optimism_corrected_CIU "0.9778581"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 1157 ASV(s)

## Removing 52 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "25"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.7162147"
## auc_optimism_corrected_CIL      "0.5860801"
## auc_optimism_corrected_CIU      "0.8050504"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.6205396"
## accuracy_optimism_corrected_CIL "0.5508295"
## accuracy_optimism_corrected_CIU "0.7017938"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 1096 ASV(s)

## Removing 50 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "37"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9271191"
## auc_optimism_corrected_CIL      "0.7769949"
## auc_optimism_corrected_CIU      "0.9973397"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8787146"
## accuracy_optimism_corrected_CIL "0.6477571"
## accuracy_optimism_corrected_CIU "0.9672643"

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 135 ASV(s)

## Removing 10 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "23"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.9294544"
## auc_optimism_corrected_CIL      "0.8121411"
## auc_optimism_corrected_CIU      "0.9869692"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8674135"
## accuracy_optimism_corrected_CIL "0.7694632"
## accuracy_optimism_corrected_CIU "0.9201563"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 17 ASV(s)

## Removing 10 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "11"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.6710997"
## auc_optimism_corrected_CIL      "0.6003336"
## auc_optimism_corrected_CIU      "0.7269684"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.6297913"
## accuracy_optimism_corrected_CIL "0.5852466"
## accuracy_optimism_corrected_CIU "0.6918579"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 71 ASV(s)

## Removing 5 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "79"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.9489647"
## auc_optimism_corrected_CIL      "0.9056533"
## auc_optimism_corrected_CIU      "0.9994898"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.920142" 
## accuracy_optimism_corrected_CIL "0.8458387"
## accuracy_optimism_corrected_CIU "0.9943038"

roc_c

Gradient boosting

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 2138 ASV(s)

## Removing 70 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 1.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   20.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9702445
## auc_optimism_corrected_CIL        0.9226123
## auc_optimism_corrected_CIU        0.9971273
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.9377335
## accuracy_optimism_corrected_CIL   0.8719025
## accuracy_optimism_corrected_CIU   0.9775253

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 1157 ASV(s)

## Removing 52 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 5.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   10.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.7531225
## auc_optimism_corrected_CIL        0.6714093
## auc_optimism_corrected_CIU        0.8180636
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.6886349
## accuracy_optimism_corrected_CIL   0.6065087
## accuracy_optimism_corrected_CIU   0.7521201

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 1096 ASV(s)

## Removing 50 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 1.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   10.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9488642
## auc_optimism_corrected_CIL        0.8990535
## auc_optimism_corrected_CIU        0.9988095
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.9107497
## accuracy_optimism_corrected_CIL   0.8546981
## accuracy_optimism_corrected_CIU   0.9945783

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 135 ASV(s)

## Removing 10 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 3.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   30.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9744834
## auc_optimism_corrected_CIL        0.9500066
## auc_optimism_corrected_CIU        0.9917358
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.9245357
## accuracy_optimism_corrected_CIL   0.8810606
## accuracy_optimism_corrected_CIU   0.9654499

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 17 ASV(s)

## Removing 10 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 3.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   20.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.7236489
## auc_optimism_corrected_CIL        0.6141361
## auc_optimism_corrected_CIU        0.7754856
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.6757193
## accuracy_optimism_corrected_CIL   0.5875751
## accuracy_optimism_corrected_CIU   0.7100322

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 71 ASV(s)

## Removing 5 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                        [,1]
## n.trees                         500.0000000
## interaction.depth                 1.0000000
## shrinkage                         0.1000000
## n.minobsinnode                   10.0000000
## auc                               1.0000000
## auc_optimism_corrected            0.9669284
## auc_optimism_corrected_CIL        0.9136975
## auc_optimism_corrected_CIU        0.9952806
## accuracy                          1.0000000
## accuracy_optimism_corrected       0.9350805
## accuracy_optimism_corrected_CIL   0.8236756
## accuracy_optimism_corrected_CIU   0.9756291

roc_c

Saving results

models_list <- list()

for (model_name in names(supplements_models$models_summ)){
  df <- do.call(rbind, supplements_models$models_summ[[model_name]])
  models_list[[model_name]] <- df
}

write.xlsx(models_list,
           file=file.path(path,paste0("supplements_models_",segment,".xlsx")),
           rowNames=TRUE)

Results overview

Alpha diversity

pc_observed[[segment]]

pc_shannon[[segment]]

pc_simpson[[segment]]

pc_pielou[[segment]]

Plots

alpha_div_plots[[paste(segment,"Country")]]

alpha_div_plots[[paste(segment,"Custom")]]

Beta diversity

Main results

pairwise_aitchison_raw[[paste("genus", segment)]]

PCA

pca_plots_list[[paste(segment,"genus custom")]]

Supplements

knitr::kable(supplements_beta[!grepl("PCoA",names(supplements_beta))],
             digits = 3,
             caption = "Supplementary PERMANOVA results")

Supplementary PERMANOVA results

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 0.963 5.058 0.033 0.001 0.002 ** pre_ltx vs post_ltx 1 0.342 1.581 0.007 0.070 0.070 post_ltx vs healthy 1 1.551 7.718 0.034 0.001 0.002 ** pre_ltx vs healthy , Country 1 1.132 5.948 0.039 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 1.991 9.200 0.042 0.001 0.001 *** post_ltx vs healthy , Country 1 1.656 8.242 0.037 0.001 0.001 *** pre_ltx vs healthy : Country 1 0.449 2.380 0.016 0.002 0.006 ** pre_ltx vs post_ltx : Country 1 0.330 1.527 0.007 0.075 0.075 post_ltx vs healthy : Country 1 0.376 1.878 0.008 0.013 0.020 *

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 1.087 3.929 0.026 0.001 0.002 ** pre_ltx vs post_ltx 1 0.439 1.457 0.007 0.045 0.045 * post_ltx vs healthy 1 1.633 5.675 0.026 0.001 0.002 ** pre_ltx vs healthy , Country 1 1.232 4.452 0.030 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 1.965 6.513 0.030 0.001 0.001 *** post_ltx vs healthy , Country 1 1.751 6.085 0.028 0.001 0.001 *** pre_ltx vs healthy : Country 1 0.525 1.912 0.013 0.002 0.006 ** pre_ltx vs post_ltx : Country 1 0.405 1.346 0.006 0.072 0.072 post_ltx vs healthy : Country 1 0.450 1.569 0.007 0.017 0.026 *

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 1010.000 3.749 0.026 0.001 0.002 ** pre_ltx vs post_ltx 1 364.559 1.485 0.007 0.003 0.003 ** post_ltx vs healthy 1 1389.311 5.466 0.025 0.001 0.002 ** pre_ltx vs healthy , Country 1 603.397 2.239 0.015 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 671.339 2.734 0.013 0.001 0.001 *** post_ltx vs healthy , Country 1 789.105 3.105 0.014 0.001 0.001 *** pre_ltx vs healthy : Country 1 364.013 1.354 0.009 0.014 0.021 * pre_ltx vs post_ltx : Country 1 352.491 1.439 0.007 0.007 0.021 * post_ltx vs healthy : Country 1 281.750 1.109 0.005 0.170 0.170

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 1.628 5.225 0.035 0.001 0.002 ** pre_ltx vs post_ltx 1 0.519 1.563 0.007 0.024 0.024 * post_ltx vs healthy 1 2.428 7.667 0.035 0.001 0.002 ** pre_ltx vs healthy , Country 1 1.136 3.644 0.024 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 1.676 5.049 0.024 0.001 0.001 *** post_ltx vs healthy , Country 1 1.667 5.264 0.024 0.001 0.001 *** pre_ltx vs healthy : Country 1 0.469 1.511 0.010 0.027 0.040 * pre_ltx vs post_ltx : Country 1 0.562 1.698 0.008 0.010 0.030 * post_ltx vs healthy : Country 1 0.432 1.367 0.006 0.064 0.064

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 1.354 3.531 0.024 0.001 0.002 ** pre_ltx vs post_ltx 1 0.528 1.327 0.006 0.024 0.024 * post_ltx vs healthy 1 1.921 4.964 0.023 0.001 0.002 ** pre_ltx vs healthy , Country 1 0.994 2.593 0.018 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 1.384 3.480 0.017 0.001 0.001 *** post_ltx vs healthy , Country 1 1.396 3.607 0.017 0.001 0.001 *** pre_ltx vs healthy : Country 1 0.514 1.344 0.009 0.020 0.030 * pre_ltx vs post_ltx : Country 1 0.571 1.439 0.007 0.010 0.030 * post_ltx vs healthy : Country 1 0.488 1.262 0.006 0.041 0.041 *

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 2.119 10.732 0.024 0.001 0.001 *** pre_ltx vs post_ltx 1 1.307 6.239 0.010 0.001 0.001 *** post_ltx vs healthy 1 3.286 17.150 0.031 0.001 0.001 *** pre_ltx vs healthy , Country 1 2.862 14.497 0.033 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 6.618 31.603 0.049 0.001 0.001 *** post_ltx vs healthy , Country 1 5.496 28.682 0.052 0.001 0.001 *** pre_ltx vs healthy : Country 1 0.783 3.993 0.009 0.012 0.018 * pre_ltx vs post_ltx : Country 1 0.562 2.691 0.004 0.112 0.112 post_ltx vs healthy : Country 1 0.998 5.252 0.009 0.002 0.006 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 2.225 7.844 0.018 0.001 0.002 ** pre_ltx vs post_ltx 1 1.382 4.680 0.007 0.002 0.002 ** post_ltx vs healthy 1 3.371 12.044 0.022 0.001 0.002 ** pre_ltx vs healthy , Country 1 3.013 10.622 0.025 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 6.474 21.918 0.035 0.001 0.001 *** post_ltx vs healthy , Country 1 5.595 19.994 0.037 0.001 0.001 *** pre_ltx vs healthy : Country 1 0.939 3.330 0.008 0.010 0.015 * pre_ltx vs post_ltx : Country 1 0.696 2.362 0.004 0.163 0.163 post_ltx vs healthy : Country 1 1.134 4.078 0.008 0.001 0.003 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 1711.535 7.089 0.017 0.001 0.002 ** pre_ltx vs post_ltx 1 799.824 3.516 0.006 0.023 0.023 * post_ltx vs healthy 1 2419.630 10.211 0.019 0.001 0.002 ** pre_ltx vs healthy , Country 1 948.424 3.928 0.009 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 1595.167 7.012 0.012 0.001 0.001 *** post_ltx vs healthy , Country 1 1870.942 7.895 0.015 0.001 0.001 *** pre_ltx vs healthy : Country 1 571.997 2.377 0.006 0.086 0.129 pre_ltx vs post_ltx : Country 1 439.338 1.934 0.003 0.768 0.768 post_ltx vs healthy : Country 1 626.621 2.653 0.005 0.023 0.069

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 3.090 9.636 0.023 0.001 0.001 *** pre_ltx vs post_ltx 1 1.948 5.972 0.010 0.001 0.001 *** post_ltx vs healthy 1 4.788 15.423 0.029 0.001 0.001 *** pre_ltx vs healthy , Country 1 2.383 7.429 0.017 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 5.328 16.338 0.026 0.001 0.001 *** post_ltx vs healthy , Country 1 4.912 15.825 0.029 0.001 0.001 *** pre_ltx vs healthy : Country 1 0.901 2.821 0.007 0.040 0.060 pre_ltx vs post_ltx : Country 1 0.830 2.551 0.004 0.131 0.131 post_ltx vs healthy : Country 1 1.362 4.417 0.008 0.003 0.009 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 2.453 6.302 0.015 0.001 0.001 *** pre_ltx vs post_ltx 1 1.695 4.312 0.007 0.001 0.001 *** post_ltx vs healthy 1 3.721 9.734 0.018 0.001 0.001 *** pre_ltx vs healthy , Country 1 2.005 5.151 0.012 0.001 0.001 *** pre_ltx vs post_ltx , Country 1 4.185 10.649 0.017 0.001 0.001 *** post_ltx vs healthy , Country 1 3.933 10.288 0.020 0.001 0.001 *** pre_ltx vs healthy : Country 1 0.906 2.336 0.006 0.086 0.129 pre_ltx vs post_ltx : Country 1 0.862 2.199 0.004 0.291 0.291 post_ltx vs healthy : Country 1 1.291 3.393 0.006 0.004 0.012 *

PCA

plot_list <- supplements_beta[grepl("PCoA",names(supplements_beta)) &
                              grepl(segment,names(supplements_beta))]

ggarrange(plotlist = plot_list,
          labels=names(plot_list),
          font.label = list(size=5,face="plain"),
          ncol=2,nrow=3)

Univariate analysis

Number of significant taxa

knitr::kable(cbind(as.data.frame(lapply(list_intersections,nrow)),
      as.data.frame(lapply(psc_effect,nrow))) %>% t() %>% 
  `colnames<-`("Count") %>% 
  `rownames<-`(c(names(list_intersections),"PSC effect ASV","PSC effect Genus","PSC effect Phylum")),caption="Number of significant taxa")

Number of significant taxa

	Count
colon genus healthy vs pre_ltx	33
colon genus pre_ltx vs post_ltx	4
colon genus healthy vs post_ltx	41
colon ASV healthy vs pre_ltx	55
colon ASV pre_ltx vs post_ltx	1
colon ASV healthy vs post_ltx	101
colon phylum healthy vs pre_ltx	2
colon phylum pre_ltx vs post_ltx	1
colon phylum healthy vs post_ltx	3
PSC effect ASV	22
PSC effect Genus	37
PSC effect Phylum	0

Counts

# univar_list <- univariate_statistics(list_intersections,
#                                      psc_effect,
#                                      ileum_genus_asv_taxa_tab)
# 
# univar_df <- univar_list[[1]]
# wb <- univar_list[[2]]
# 
# # save the results
# saveWorkbook(wb,"results/Q1/DAA_final_terminal_ileum.xlsx", overwrite = TRUE)
# 
# # see the results
# univar_df

Machine learning

Main models

Summary

knitr::kable(models_summ_df_colon %>% dplyr::select(
"alpha","lambda",
"auc_optimism_corrected",
"auc_optimism_corrected_CIL",
"auc_optimism_corrected_CIU"),
             digits=3,caption="Elastic net results")

Elastic net results

	alpha	lambda	auc_optimism_corrected	auc_optimism_corrected_CIL	auc_optimism_corrected_CIU
pre_ltx vs healthy ASV colon	0.6	0.006	0.900	0.863	0.955
pre_ltx vs post_ltx ASV colon	0.4	0.002	0.777	0.722	0.835
post_ltx vs healthy ASV colon	0.4	0.009	0.913	0.726	0.997
pre_ltx vs healthy genus colon	1.0	0.002	0.939	0.874	0.972
pre_ltx vs post_ltx genus colon	0.4	0.005	0.720	0.610	0.811
post_ltx vs healthy genus colon	0.2	0.001	0.944	0.853	0.992

ROC - ASV level

roc_curve_all_custom(roc_cs[c(7:9)], 
                     Q="Q1",
                     model_name="enet_model")

## [1] "pre_ltx vs healthy ASV colon"  "pre_ltx vs post_ltx ASV colon" "post_ltx vs healthy ASV colon"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

ROC - Genus level

roc_curve_all_custom(roc_cs[c(10:12)],Q="Q1",
                     model_name="enet_model")

## [1] "pre_ltx vs healthy genus colon"  "pre_ltx vs post_ltx genus colon" "post_ltx vs healthy genus colon"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

Supplementary models

Summary

# Build final dataframe
models_list[["enet_model"]] <- rbind(models_summ_df_ileum,models_summ_df_colon)
final_df <- tibble(row_names = rownames(models_list[[1]]))

# Loop through models and extract required values
for (model_name in names(models_list)) {
  model_df <- models_list[[model_name]]
  
  # Combine AUC_optimism_corrected with its CI values
  final_df[[model_name]] <- paste0(
    round(model_df$auc_optimism_corrected, 3), 
    " (", round(model_df$auc_optimism_corrected_CIL, 3), "; ", 
    round(model_df$auc_optimism_corrected_CIU, 3), ")"
  )
}

knitr::kable(final_df, caption="All models")

All models

row_names	knn_model	rf_model	gbm_model	enet_model_ra	knn_model_ra	rf_model_ra	gbm_model_ra	enet_model
pre_ltx vs healthy ASV terminal_ileum	0.863 (0.775; 0.946)	0.933 (0.887; 0.978)	0.952 (0.924; 0.979)	0.924 (0.837; 0.972)	0.829 (0.699; 0.932)	0.975 (0.95; 0.993)	0.969 (0.947; 0.995)	0.957 (0.926; 0.99)
pre_ltx vs post_ltx ASV terminal_ileum	0.676 (0.594; 0.772)	0.871 (0.83; 0.923)	0.872 (0.83; 0.921)	0.734 (0.617; 0.834)	0.666 (0.583; 0.736)	0.901 (0.85; 0.953)	0.882 (0.825; 0.933)	0.874 (0.817; 0.923)
post_ltx vs healthy ASV terminal_ileum	0.852 (0.82; 0.896)	0.888 (0.839; 0.954)	0.904 (0.825; 0.945)	0.888 (0.818; 0.94)	0.824 (0.743; 0.909)	0.925 (0.878; 0.964)	0.915 (0.849; 0.959)	0.95 (0.901; 0.971)
pre_ltx vs healthy genus terminal_ileum	0.891 (0.822; 0.948)	0.939 (0.897; 0.986)	0.972 (0.949; 0.994)	0.939 (0.896; 0.979)	0.819 (0.765; 0.874)	0.967 (0.945; 0.986)	0.97 (0.942; 0.991)	0.977 (0.96; 0.995)
pre_ltx vs post_ltx genus terminal_ileum	0.77 (0.668; 0.854)	0.829 (0.776; 0.873)	0.825 (0.763; 0.872)	0.737 (0.654; 0.812)	0.718 (0.66; 0.792)	0.86 (0.825; 0.904)	0.835 (0.784; 0.889)	0.841 (0.765; 0.895)
post_ltx vs healthy genus terminal_ileum	0.904 (0.847; 0.929)	0.887 (0.84; 0.932)	0.921 (0.883; 0.966)	0.857 (0.801; 0.894)	0.801 (0.734; 0.886)	0.916 (0.847; 0.982)	0.947 (0.907; 0.983)	0.945 (0.924; 0.969)
pre_ltx vs healthy ASV colon	0.776 (0.678; 0.846)	0.95 (0.889; 0.99)	0.948 (0.897; 0.989)	0.948 (0.899; 0.974)	0.854 (0.723; 0.935)	0.979 (0.934; 1)	0.97 (0.923; 0.997)	0.9 (0.863; 0.955)
pre_ltx vs post_ltx ASV colon	0.579 (0.534; 0.637)	0.681 (0.58; 0.747)	0.742 (0.699; 0.817)	0.62 (0.463; 0.703)	0.542 (0.46; 0.63)	0.716 (0.586; 0.805)	0.753 (0.671; 0.818)	0.777 (0.722; 0.835)
post_ltx vs healthy ASV colon	0.861 (0.757; 0.977)	0.87 (0.778; 0.952)	0.936 (0.882; 0.999)	0.933 (0.836; 0.995)	0.888 (0.842; 0.96)	0.927 (0.777; 0.997)	0.949 (0.899; 0.999)	0.913 (0.726; 0.997)
pre_ltx vs healthy genus colon	0.847 (0.761; 0.888)	0.889 (0.762; 0.95)	0.951 (0.867; 0.99)	0.932 (0.876; 0.968)	0.859 (0.781; 0.919)	0.929 (0.812; 0.987)	0.974 (0.95; 0.992)	0.939 (0.874; 0.972)
pre_ltx vs post_ltx genus colon	0.521 (0.424; 0.607)	0.679 (0.612; 0.76)	0.709 (0.652; 0.755)	0.569 (0.492; 0.71)	0.567 (0.495; 0.644)	0.671 (0.6; 0.727)	0.724 (0.614; 0.775)	0.72 (0.61; 0.811)
post_ltx vs healthy genus colon	0.899 (0.825; 0.988)	0.922 (0.867; 0.962)	0.932 (0.87; 0.997)	0.931 (0.829; 1)	0.876 (0.811; 0.957)	0.949 (0.906; 0.999)	0.967 (0.914; 0.995)	0.944 (0.853; 0.992)

write.csv(final_df,file=file.path(path,"AUC_all_models.csv"),row.names = FALSE)

ROC - ASV

rocs_list <- supplements_models$roc_cs
rocs_list[["enet_model"]] <- roc_cs

plot_list <- list()

for (model_name in names(rocs_list)) {
  plot_list[[model_name]] <- roc_curve_all_custom(rocs_list[[model_name]][c(1:3)],
                       Q="Q1",
                       model_name=model_name)
}

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

ggarrange(plotlist = plot_list,labels = names(rocs_list),font.label = list(face="plain",size=7))

ROC - genus

plot_list <- list()

for (model_name in names(rocs_list)) {
  plot_list[[model_name]] <- roc_curve_all_custom(rocs_list[[model_name]][c(4:6)],
                       Q="Q1",
                       model_name=model_name)
}

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

ggarrange(plotlist = plot_list,labels = names(rocs_list),font.label = list(face="plain",size=7))